AN INVESTIGATION OF FETAL GROWTH IN RELATION TO PREGNANCY CHARACTERISTICS

AN INVESTIGATION OF FETAL GROWTH IN RELATION TO

PREGNANCY CHARACTERISTICS by

Joe Max Mongelli MB BS, B Sc (Sydney) MRCOG

Thesis submitted to the University of Nottingham for the degree of Doctor of Medicine, November 1994

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CONTENTS Title page 1 Contents 2 Abstract 3 Acknowledgements 4 Abbreviations 5 Part I - Literature Review Chapter 1 The Determinants of Birth Weight 6 Chapter 2 Adjustable Birth Weight Standards 15 Chapter 3 Ultrasonic Methods of Fetal Weight Estimation 22 Chapter 4 Models of Fetal Growth 29 Chapter 5 Screening Strategies for Abnormal Fetal Growth 37 Part II- Development of Research Techniques Chapter 6 Principles of the Customised Growth Chart 49 Chapter 7 Methods of Gestational Age Estimation 57 Chapter 8 Forward Projection of Fetal Weight Estimate 64 Chapter 9 Selection of Ultrasonic Weight Formula 68 Chapter 10 Ultrasonic Study of Fetal Growth: Patients and

Methods. 76

Part III - Clinical Findings Chapter 11 An Ultrasound Standard for Fetal Weight Gain 83 Chapter 12 Symphysis-fundus Height in Relation to

Gestational Age and Fetal Weight 95

Chapter 13 Fetal Growth Kinetics in Relation to Pregnancy Characteristics

102

Chapter 14 Customised Growth Charts in Relation to Neonatal Outcome

115

Chapter 15 The Prediction of Birth Weight 126 Part IV - General Discussion Chapter 16 Comments and Conclusions 143 References 156

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1. THE DETERMINANTS OF BIRTH WEIGHT

1.1 Introduction

Birth weight is one of the most important measures we have of the

health status of a population, being a strong predictor of both mortality

and morbidity, and reflecting nutritional status and growth rates. Yet

the estimation of the normal growth potential -and hence the definition

of growth retardation - for a given individual has remained an elusive

objective.

Neonatal size can be influenced by a large number of variables.

Kramer (1987), in a lengthy review on low birth weight, listed 43

potential causes, subdivided into 7 groups, while admitting that his

literature search may not have been complete. For the purposes of our

discussion, we will attempt to classify them as pathological or

physiological, depending whether or not they are associated with

adverse perinatal outcome. This classification will be arbitrary for

many of these factors, because of our limited knowledge in this field.

1.2 Pathological Factors

A large number of pregnancy complications are associated with

reduced birth weight. Classically, growth retardation has been

classified as either symmetric or asymmetric (Pearce & Campbell,

1985), depending on whether the fetal body dimensions are

proportionately reduced, or whether some degree of ‘head sparing’ has

occurred. In practice, asymmetric IUGR is associated with either pre-

eclamptic toxaemia or recurrent abruption, while most other causes

lead to symmetric IUGR.

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Hypertensive Disease.

Essential, uncomplicated hypertension poses little or no risk to the

fetus. In early onset or severe pre-eclampsia birth weight may be

reduced by 300-500g and birth length by 1-3 cm, whereas late onset or

mild pre-eclampsia has no such association (Fedrick &

Adelstein,1978; Long et al, 1980). Reduced utero-placental blood flow

is considered to be responsible for reduced growth.

Chronic Maternal Illness

Maternal cyanotic heart disease is associated with fetal growth

retardation in up to 52% of pregnancies, as opposed to 9% in the

acyanotic group (Shime et al, 1987).

Diabetes has complex effects on fetal growth, with a tendency towards

larger babies unless associated with vascular disease and advanced

maternal age, when growth retardation is more likely. It is the main

pathological cause of fetal macrosomia.

Severe chronic respiratory diseases such as poorly controlled asthma

(Greenberger and Patterson, 1983), cystic fibrosis (Palmer et al, 1983)

and bronchiectasis (Thaler et al, 1986) may lead to reduced fetal

growth.

In the case of anaemia, it is difficult to separate the effects of

anaemia per se from the underlying nutritional problems. However,

women with sickle cell disease, sickle-thalassaemia and sickle

haemoglobin-C disease have an increased incidence of growth

retarded infants (Powers et al, 1986).This is most likely due to

placental micro-infarcts resulting from episodes of sickling, leading to

placental insufficiency.

Chronic renal disease of moderate severity is associated with IUGR in

up to 24% of cases (Katz et al, 1989), although some of this effect

may be related to hypertension.

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Systemic lupus erythematosus has been implicated in fetal growth

retardation (Carlson,1988), though this may be a result of the

underlying renal disease or drug therapy.

Maternal Addictions

Smoking mothers have babies that are 150 - 200g lighter than those of

non-smokers (Wilcox et al,1993a); this appears to be caused by

smoking in itself rather than other factors associated with the smoker

(Doughterty,1982).

Excessive alcohol intake may result in small babies with shortened

palpebral fissures and a small head - the fetal alcohol syndrome

(Lemoine P et al, 1968). Fetal weights are reduced by 165-200 grams

among mothers who drink the equivalent of more than 50 ml of

absolute alcohol per day (Ouellette et al, 1977).

Heroin addiction has also been associated with reduced fetal growth

(Naeye et al, 1973).

High Altitude

Babies born at high altitude are lighter than those born at ground level

(Lubchenko, 1963); this appears to be a 'dose-response' effect related

to chronic hypoxia, with smaller babies being born at higher altitude

(Yip, 1987). Interestingly, both of these studies showed higher rates of

preterm delivery.

Malnutrition

Maternal malnutrition, when severe, may result in adverse neonatal

outcome. Stein and Susser (1975) analysed the birth statistics in

Holland during the famine of 1944-1945; they found that birth weights

declined by about 10% only when under-nutrition occurred in the third

trimester with caloric intake below 1500 g. This is to be expected,

since the period of greatest absolute growth is in the last 10 weeks of

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gestation, when the average fetus gains about 2000g during this

interval (Hadlock et al, 1991).

Placental Disorders.

Recurrent antepartum haemorrhages in the first and second trimesters

is strongly related to reduced fetal growth, possibly due to impaired

development of the utero-placental circulation (Fedrick & Adelstein,

1978). Other placental anomalies associated with SGA infants include

circumvallate placenta, and large chorioangiomas.

Infection

Viral infections, particularly rubella and cytomegalovirus, may reduce

fetal weight and length to 80-85% of normal values ( Miller, 1981;

Naeye, 1967). In fatal cases of rubella, the growth restriction is

associated with markedly reduced cell numbers in the fetal organs

(Naeye, 1965). Listeriosis is sometimes associated with IUGR.

In global terms, malaria is probably the most important infectious

agent associated with growth restriction on a world-wide basis. This is

related to haemolytic anaemia and placental insufficiency related to

placental infestation. The pathological changes observed in the

placenta include perivillous fibrinoid deposits, syncytiotrophoblast

necrosis and partial loss of microvilli. A brownish discoloration may

be observed (plasmodial placental pigmentation), and these cases are

associated with significantly lower birth weights (Garin et al, 1985;

Walter et al,1982).

Chromosomal Defects

Most chromosomal and genetic disorders are associated with impaired

fetal growth of varying severity. In Downs’ syndrome the birthweight

is 80-90% of normal, while for trisomy 13 and Turner’s syndrome it is

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80% and 84% respectively (Polani, 1974). More severe growth

restriction is observed in foetuses with trisomy 18, with an average

birth weight of only 62% of normal. Some genetic disorders such as

the Seckel and Russell-Silver syndromes are associated with severe

dwarfing apparent at birth.

On the opposite side of the spectrum, we find a group of genetic

disorders that are associated with fetal growth acceleration. These

include the Beckwith-Wiedemann syndrome, in which trisomy for the

IGF-II gene has been implicated, and ‘stood’ conditions associated

with a dramatic increase of fibrous tissue (Elejalde et al, 1977).

In Sotos’ syndrome, characterised by cerebral gigantism , the birth

weight is not significantly increased but the birth length is increased to

a mean of 55.2 cm.

1.3 Physiological Factors

Duration of pregnancy.

The length of gestation is the most important determinant of birth

weight (Wilcox et al,1993b), and also of perinatal mortality and

morbidity in the pre-term period (Allen et al, 1993). The 'terminal

flattening’ seen in birth weight standards based on menstrual data is

artefactual; it is much less marked in standards derived from

ultrasound-dated populations (Wilcox et al, 1993a).

Parental size.

The relationships between birth weight and parental size have been

studied extensively, both in humans and in animals. The classic

studies by Walton and Hammond (1938) on crosses between the Shire

horse and the Shetland pony showed that the birth weights of foal born

to Shetland dams of Shire sires were close to those of pure Shetlands;

conversely, foals of shire dams by Shetland sires were close to those

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of the pure breed. Evidence in humans on the preponderance of the

maternal effects on fetal growth has been presented by Cawley (1954)

and Ounsted (1966). Both maternal height and weight have positive

correlations with birth weight, the latter being the stronger factor.

Low maternal pre-pregnancy weight is significantly correlated with

both preterm delivery and low birth weight (Garn 1990).

Obesity, as measured by the body mass index, is only weakly

correlated (Abrams, 1986; Wilcox et al, 1993b). The parents' own

birthweight is significantly correlated with that of their offspring

(Alberman et al, 1992).

Parity.

The positive effect of parity on birth weight has been documented in

most races and many mammalian species (Ounsted, 1973; Bantje,

1985), suggesting that its mechanism may have an evolutionary

advantage. Garn (1990) has argued, on epidemiological grounds, that

the effect of parity is a result of the increase in the maternal pre-

pregnancy weight seen in developed countries, rather than an

independent factor. This does not agree with multiple regression

analysis of birth weight, which shows parity to be a factor independent

of mid-pregnancy weight (Wilcox et al, 1993b); this may be because

the mid-pregnancy weight is a compound variable, dependent on both

the pre-pregnancy weight and the maternal weight gain.

There is some evidence that the effect is partner-specific, i.e. a change

in partner may be associated with a reduction in the birth weight of the

first born of the new relationship (Warburton & Naylor, 1971).

It may be argued that because of the significant increase in the

perinatal mortality of first-born infants and those of mothers of high

parity, this factor should be classified as a pathological variable. The

odds ratios, however are only slightly elevated (Kirkup &

Welch,1990), and do not justify this re-classification.

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Race.

Meredith (1970) published an extensive description of the variations

in birth weight among different ethnic groups. Of the 78 groups

considered, the largest newborns were found in Anguilla and Nevis,

weighing a mean of 3.88 Kg. The smallest babies were those of the

Lumi tribe in the Toricelli mountains in New Guinea, with a mean

birth weight of 2.4 Kg. These ethnic differences clearly persist in

mixed populations from the same location (Cheng et al, 1972; Wilcox

et al, 1993b). Birth weight variations do not always correlate with

trends in perinatal mortality. In Singapore, Malay babies have a much

higher perinatal mortality than Indian babies even though their

percentage of low-birthweight (<2500) is significantly smaller

(Hughes, 1984), both groups living in similar socio-economic

conditions with total health care coverage. Similarly, Californian black

babies under 3001g have much lower mortality rates than whites, even

though their birthweights are lower (Williams et al, 1982).

Sex

The female newborn weighs on the average 118 g less than the male

(Wilcox et al, 1993b), and this has been observed in most ethnic

groups studied (Meredith, 1970).Animal studies suggest that the XY

embryo has a growth advantage at the earliest stages of organogenesis

(Snow, 1989); hormonal differences are not responsible, since the sex

differences are noted even in anencephalic foetuses. It is of interest

that in spite of this, females born preterm have lower mortality and

morbidity than males (Allen et al, 1993). This may be due to female

infants having relatively more energy stores in adipose tissue than

males (Oakley et al, 1977).

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Others

Women with a history of SGA infants are more likely to give birth to

small babies. It is not clear whether this is due to genetic factors, or

recurrence of genetic/pathological factors. Work performed by

Ounsted (1965,1966) has shown that mothers who have borne SGA

infants had themselves lower than average birth weights, although

their adult height did not differ significantly from that of women who

had given birth to babies of normal weight.

Consanguinity in parents has been shown to cause a significant

reduction in birth weight in Pakistan (Shami et al, 1991), Japan

(Morton, 1958) and Norway ( Magnus et al, 1985).

A seasonal trend has been observed in birth weights, with significantly

lower values in summer and a peak in winter/early spring (Matsuda,

1992). These fluctuations are rather minor, occurring within a 100g

band.

1.4 Discussion

The distinction between 'pathological ' and 'physiological' factors is

to some extent arbitrary, with a significant 'grey zone' of uncertainty.

In terms of defining normal growth potential, the genetic components

of the physiological factors are probably more important, and this was

stressed by Lazar and colleagues (1975). This distinction is, however,

an important exercise in order to develop valid adjustable growth

standards. It is likely that fetal development is under the control of

inhibitory and stimulatory growth factors, and that some physiological

and pathological factors may well act through common pathways.

Animal studies have shed some light on the relative importance of

fetal genome and maternal effect; these have been reviewed by Snow

(1989). There is good evidence to suggest that maternal effects operate

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late in gestation, whereas early fetal growth is controlled by fetal

genetic mechanisms. Winick (1971) studied the cellular basis of fetal

growth using animal models. Three phases of growth were identified:

cellular hyperplasia, followed by both hyperplasia and hypertrophy,

and then predominantly hypertrophy. Depending on the timing and

duration of experimental insults, different forms of growth restriction

were observed. Disturbances in early pregnancy restricted the total

cell number, so that no 'catch-up' growth was possible, whereas later

in pregnancy cell size was predominantly affected with minimal

reduction in cell numbers , and post-natal recovery was possible with

adequate nutrition. This points to the heterogeneous nature of growth

disturbances and to the need for using appropriate standards.

It is difficult to determine how much of the differences observed

among different ethnic groups are due to genetic factors, as opposed to

environmental factors such as nutrition and socio-economic

conditions. Hence customising for ethnicity can only be justified when

the adjustment factors are derived from sub-populations in the same

location and ideally living under similar socio-economic conditions.

The observed differences in neonatal morbidity between different

sub-populations do not always agree with the birth weight differences.

This lends weight to the argument that, for optimal performance, fetal

weight for gestation as an index of morbidity needs to be evaluated in

relation to other pregnancy characteristics.

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2. ADJUSTABLE BIRTH WEIGHT STANDARDS

2.1 Introduction

Birth weight on its own is only a crude indicator of neonatal welfare,

being a retrospective measure from which it is difficult to make

accurate inferences about prenatal growth kinetics. The definition of

'low birth weight infant' as being a newborn weighing below 2500g

was used as an index of prematurity until the 60's, when it was

realised that a considerable proportion of these cases were in fact

growth restricted (Ounsted, 1970). It has been nevertheless a

convenient tool for epidemiologists, since reliable data on the

duration of gestation is often difficult to obtain, particularly in

developing countries. This, however, fails to make the important

distinction between infants who are small because they were born

preterm and those term babies who are small because of constitutional

or pathological factors.

2.2 Pathological Implication of Abnormal Birth Weight

This has prompted the search for birth weight for gestation standards,

so that given the appropriate variables this distinction can be made.

When birth weight is analysed as a function of gestation, some

important relationships with poor perinatal outcome emerge. In a large

study by Patterson et al (1986) of a database of 44 811 cases, a

U-shaped relationship was found between birth weight centile and the

incidence of morbidity , with minimal morbidity near the middle

ranks of birth weight ; the percentage of the total poor perinatal

outcome occurring below the tenth or above the 90th centiles

increased linearly from 16% at 28-29 weeks to 57% at 40-41 weeks.

There is mounting evidence that being small for gestational age has

pathological implications extending into childhood and adulthood.

Hill et al (1984) , in a small study, related the outcome of term infants

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with nutritional parameters. Malnutrition in the newborn was defined

in terms of subcutaneous tissue thickness. About 45% of the 33

malnourished infants had birth weights below the 10th centiles; in this

group, poor outcome included reduced educational achievement up to

the age of 14. Rantakallio(1985) studied a cohort of 12,000 children

in Finland followed up to 14 years; it was found that the incidence of

neuro-behavioural disturbances was significantly higher in weight

percentile classes below the median. Most of these and similar studies

are flawed by the use of menstrual dates in determining gestation . As

the error tends to be towards overestimation (Gardosi & Mongelli,

1993), more babies would be classified incorrectly as below the 10th

centile than those assigned above the 90th centile.

2.3 Assignment of Gestational Age

With very few exceptions, gestation is usually estimated from

menstrual dates, rounded down to 'completed weeks'. Typically, when

birth weight is plotted against gestation, the resulting standard curves

show considerable flattening near term, and this has been attributed

either to placental ageing/insufficiency, or to physical restriction of

growth. More recent standards in which gestational age has been

calculated on the basis of early ultrasound measurements show a

much more linear relationship between duration of pregnancy and

birth weight (Lindgren, 1988; Wilcox et al,1993a). This inaccuracy in

the estimation of gestational age is also likely to lead to a greater

apparent variance in the birthweight distribution for any given week of

gestation.

2.4 Preterm Delivery and Birth Weight Standards

Birth weight standards have in the past been referred to as 'fetal

growth curves'. Apart from the fact that these are cross-sectional

studies, values derived from preterm deliveries cannot be regarded as

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representative of normal growth. Furthermore, unless the population

sample is very large, the number of babies born preterm is relatively

small, leading to a greater incidence of sampling errors. Preterm

delivery may be associated with growth restriction , and birth weight

norms at these gestations may be well below those derived from serial

ultrasound weight estimations (Ott, 1993). An experimental model of

growth retardation supporting this epidemiological data was described

by Alexander (1964). He, and subsequent workers, found that the

excision of endometrial caruncles in the sheep (before pregnancy)

resulted not only in an increased rate of growth retardation, but also in

increased preterm labour rates and intrauterine death.

Another indicator of pathology in the preterm period is the statistical

distribution of birth weights. Whereas the distribution of birth weights

at term shows a significant positive skewness, in the preterm period

this becomes negative (Wilcox et al, 1993a), most likely because of

the greater number of growth-retarded babies born at these gestations.

It has also been shown that preterm babies delivered electively are

significantly lighter than those born spontaneously, yet even when the

former are excluded from the analysis the negative skewness is

reduced but not entirely eliminated (Yudkin, 1987).

2.5 Effect of Environment

Reference standards may also be strongly affected by the environment

and population characteristics. For example, Lubchenko's (1962) birth

weight chart has been used widely in the United States and elsewhere.

This was derived from a population in Denver, Colorado, at an altitude

of about 10000 ft. Not only did this high altitude result in lower birth

weights for all gestations than any other published standard, but also

the percentage of births occurring preterm was much higher than

expected. There is also some evidence that birth weight standards may

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change historically as living standards and social characteristics vary

(Alberman 1991; Ulizzi & Terrenato, 1992).

2.6 Adjustable Standards

The realisation that these genetic or physiological effects are in

operation led to attempts to introduce adjustment factors to allow for

maternal characteristics. Thomson and colleagues (1968) published

birth weight for gestation tables allowing for sex, parity and inclusive

of correction factors for maternal weight . Altman and Coles (1980)

produced nomograms for the calculation of birth weight centiles based

on this data that included correction factors for parity, maternal height

and weight, and fetal sex.

Lazar and colleagues (1975) used multiple regression analysis to

derive correction factors for both maternal and paternal weight and

height, claiming that paternal weight is as important as maternal

weight; they believed that the effect of these variables is largely of

genetic origin, and in order to improve their predictive power they

estimated what the parental values of height and weight would be at

the age of 20 before entering them in their regression model. Parity

and ethnic group were not considered in their analysis, and their model

was not tested prospectively.

Voigt (1989) and Mamelle(1989) published elaborate tables to allow

adjustment for these variables, but the fact that they are not in general

use attests to their complexity.

Some interesting similarities among different birth weight standards

were described by Dunn (1989). When the centile cut-off points were

expressed as percentages above or below the population median and

plotted against gestation, virtually identical values were obtained for

all the standards. This remarkable correspondence led to the

construction of the Bristol Perinatal Growth Chart, a method that

would allow the production of antenatal and post-natal growth

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standard for population sub-groups. The method assumes that the

latter are normally distributed.

2.7 The Birth Weight Ratio

An alternative variable to describe size for gestation , the 'birth weight

ratio' , was described by Brooke and colleagues (1989) in order to

analyse factors affecting birth weight. This is simply the observed

weight divided by the weight expected for a given gestation. Morley

and colleagues (1990) described a relationship between birthweight

ratio and the need for mechanical ventilation and post-neonatal

mortality in preterm infants; this, however, was not observed in the

study of Brownlee et al (1991). More recently, Wilcox and colleagues

(1993b) analysed a large database of 31 561 computerised records of

term deliveries in order to develop a multiple regression model to

predict birth weight. The variables included gestation, sex, maternal

height, weight, parity and ethnic group. The ratio of the observed

birth weight to predicted weight ('individualised birth weight ratio',

IBR) can then be calculated by a computer program and expressed as a

centile value. This method has been reported to identify a higher

proportion of truly growth retarded infants, as defined by neonatal

ponderal index and skinfold thickness measurements (Sanderson,

1994). The drawback of Wilcox's program is that in its present form it

is only applicable to babies born at term, and cannot be used for

screening purposes in the antenatal period.

It can be shown that when birth weight ratios are transformed into

centile values, these are very similar to the corresponding birth weight

centiles, provided the reference standards used to obtain the mean and

standard deviation are similar (chapter 16).

2.8 Discussion

The large number of birth weight standards in existence is a reflection

of the importance given to this parameter, as well as the need to relate

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birth weight to local conditions. In the English literature alone,

Goldenberg and colleagues (1989) were able to review 13 such

standards published since 1963. Large differences were noted in the

10th centile cut-off point, greater than 500g for some gestational ages.

These discrepancies are partly due to inconsistencies in methodology.

For example, McKeown and Gibson (1951) included both live and

still births in their analysis of the Birmingham data, whereas most

investigators have restricted their samples to live born infants. The

treatment of outliers in the data can vary between studies. A variety of

corrections for bimodal or skew birthweight distributions have also

been adopted (Gruenwald, 1966; Milner & Richards, 1974). A number

of studies are also limited by small sample sizes, making it difficult to

estimate centile distributions of birthweight with any degree of

accuracy.

Another major source of error is gestational age assignment.

Assessment of fetal well-being, by whatever means, requires an

accurate estimate of gestational age. The introduction of routine early

ultrasound scanning in the United Kingdom has eliminated large

errors, but the use of '10-day' or '7-day' rules whereby menstrual dates

are used in preference to ultrasound determined dates if in agreement,

may lead to some loss of accuracy (see Chapter 7). Those women who

book late tend to have poorer outcomes, and ultrasonography may be

of special benefit in this group. Although algorithms have been

developed for the accurate determination of gestational age up until 32

week's gestation (Sabbagha et al, 1978), these have not gained

widespread acceptance.

All of the adjustable standards of fetal growth are limited by their use

of cross-sectional birth weight data. While they may be valid for the

assessment of relative size, they are not suitable for assessing serial

weight estimates, i.e. growth (Altman, 1994).

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With the exception of the IBR, the correction factors are usually

presented in tabular form, and do not take into account gestation-

dependent variations. Thomson and colleagues (1968) stated that these

adjustments should not be made for gestations under 37 weeks, since

their numbers was limited in that range. Nevertheless, their parity

differences were statistically different even at 32 weeks. Even if there

were sufficient numbers, it is doubtful that these adjustment factors

would be applicable to intrauterine fetal weight estimates. In practice

most clinicians do not adjust beyond sex and parity, probably because

of the inconvenience in using complex tables or graphs. In the

standard published by Yudkin and colleagues (1987) - widely used in

paediatric units in the UK-, no adjustment is made apart from fetal

sex.

Although the importance of accurate and valid fetal growth standards

has long been acknowledged, the validity of specific growth standards

when applied to a particular population or study sample is seldom

tested. As a result, the assessment of growth retardation and evaluation

of screening procedures may be inaccurate and biased.

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3. ULTRASONIC METHODS OF FETAL WEIGHT

ESTIMATION

3.1 Introduction

Fetal weight estimation plays an important part in clinical obstetrics

decision-making, often used in screening for IUGR, the management

of diabetes in pregnancy and pregnancy complicated by breech

presentation. In current practice, ultrasonography remains the most

accurate method for determining the estimated fetal weight (EFW).

Although newer imaging techniques such as computerised tomography

and nuclear magnetic resonance are likely to be much more accurate

(Baker et al, 1994), their cost will prevent widespread use; their main

role in the immediate future will remain as research tools. Three-

dimensional ultrasound equipment, on the other hand, is now

affordable, and the better, more reliable definition of anatomical

planes (Kuo, 1991) should lead to reduced operator error and

hopefully to better performance of the existing formulae.

3.2 Fetal Weight Estimation Formulae

The equations for fetal weight estimation in terms of given ultrasound

parameters are usually derived by applying a model of fetal weight

composition to a source population examined shortly before delivery.

The measured ultrasound parameters and the birth weights, are entered

and the relevant coefficients are estimated by multiple regression

analysis. The performance of the formula in term of its prediction

errors is then tested on a separate sample, and 'target' population. One

of the first such formulae to be developed was that of Campbell &

Wilkin (1975 ); this was based on the fetal abdominal circumference

(AC), and it is still in common use in the UK A considerable number

of other formulae that usually employ more than one ultrasound

parameter have since been published. A sample of these are listed in

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table 3.1. They can be broadly classified as exponential or non-

exponential, depending on the type of mathematical expression.

Exponential formulae take the form of:

EFW = exp[F(P1,..Pn)]

where F(p1,..pn) is a polynomial function of the ultrasound parameters

P1 to Pn.

Other approaches to weight prediction have been explored. A

computer neural network program has been developed specifically to

estimate weights in foetuses at risk of macrosomia (Farmer et al,

1992); ultrasound parameters were combined with clinical

measurements such as fundal height, with a reported accuracy of

around 5%. Birnholz (1986) published an algorithmic method for

weight estimation, whereby one of two formulae are chosen by a

computer program depending on the body proportions of the

individual. About 90% of cases had an error less than 80 g/Kg . This

method requires regression analysis of the fetal ultrasound parameters

in the population under study.

3.3 Clinical Performance of Weight Estimation Formulae

This is usually assessed by the statistical analysis of the errors. They

may be expressed as signed or absolute percentage errors, absolute

error in grams, errors in grams per Kg of fetal weight, and percent of

errors beyond a given threshold. A typical weight formula employing

more than one parameter will estimate 75% of cases within 15% of the

actual weight (Thompson et al, 1990).The most common practice is to

report the mean error and its standard deviation (SD); the former gives

a measure of the tendency to under- or over-estimate (the systematic

error), whereas the latter indicates the spread of the errors. It has

recently been suggested that the standard deviation should be replaced

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by the 95% confidence limit of the errors (Bland & Altman, 1986);

this is certainly preferable in situations when the distribution is not

Gaussian. There is general agreement that equations employing two or

more ultrasound parameters are more reliable than those using only

one (Hadlock et al, 1985; Guidetti et al, 1990). Formulae developed

within an institution tend to perform better than those from other

centres (Thompson et al, 1990), probably because of significant inter-

observer variability (Chang et al, 1993) and differences in equipment

and populations. For example, formulae derived from Chinese

populations perform better on Chinese patients than those developed

from European populations (Chang et al, 1991). It has been reported

that continual review of the results obtained by the methods used by an

obstetric ultrasound department may further enhance its performance

(Thompson et al, 1990). In Hadlocks' studies (1985), the prediction

errors of equations employing three or four ultrasound parameters

(BPD, HC, FL and AC) had a SD of around 8%. Slightly better values

were reported by Issel and colleagues (1991), a SD of about 7% by

measuring up to 7 ultrasound parameters. In clinical practice these

errors tend to be somewhat higher (Miller et al, 1988). The problem

of systematic over- or under-estimation of fetal weight is frequently

reported when such formulae are used by centres other than the one

where the formula originated (Robson et al, 1993). Some of this error

may be due to the variations in the lag times between ultrasound

examination and delivery, which is not usually allowed for by the

authors of the formulae; this means that a fetus examined some days

before delivery will be slightly lighter than at birth, and when the birth

weight is entered into the regression analysis without due

modification, a small but appreciable over-estimation will take place.

This problem was appreciated by Spinnato and colleagues (1993), who

introduced a time component into the established formulae, valid up to

35 days before delivery. A more serious and common problem is the

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existence of trends in the errors. There may be significant and

negative correlation with the size of the fetus (Robson et al, 1993;

Miller et al, 1988); hence small babies tend to be overestimated while

large ones are under-estimated. This can lead to serious data distortion

when producing normal values in growth curves for fetal weight, and

may affect the performance of screening programs for small- and

large- for gestational age infants.

3.4 Discussion

Early retrospective studies on the detection of growth retarded

foetuses by measuring the biparietal diameter suggested that this

parameter could be helpful in defining groups of cases with higher

perinatal mortality and preterm delivery rates (Persson et al, 1978).

The technique, however, was subsequently found by Campbell &

Dewhurst (1971) to have a false positive rate for SGA of 25%. This is

not surprising, since the correlation coefficient of BPD with birth

weight is not as high as other ultrasound parameters such as the

abdominal circumference and femur length (Favre et al, 1993).

Several studies have been published on the performance of different

ultrasound parameters in the detection of the SGA fetus. Neilson and

colleagues (1984) measured a series of fetal parameters in the third

trimester; they found that the product of trunk area and crown-rump

length (as an index of fetal weight) was superior to the trunk diameter

alone. Dudley and colleagues (1990) showed that EFW was the best of

four ultrasound parameters in identifying the small-for-dates infant.

Similarly, Chang and colleagues (1993) reported that a single EFW

estimate based on multiple ultrasound parameters was superior to

abdominal circumference in predicting 2 out of 3 indices of neonatal

nutritional deprivation. The efficacy of growth screening programs

continues to be limited by the error of ultrasonic EFW and by the lack

of a uniform standard for fetal growth and growth velocity. The

26

normal standards of EFW published to date show considerable

disagreement, and at least some of the differences may be attributable

to the choice of weight estimation formula. This is further discussed in

chapter 9.

Volumetric formulae for fetal weight estimation have been proposed

by several workers, including Combs (1993 ) and Birnholz (1986), on

the grounds that fetal volume is proportional to weight when the

specific gravity is constant. There are at least two theoretical

objections to this argument. Firstly, the gestational age-dependent

changes in specific gravity have not been described, and the magnitude

of error from this factor is unknown. Secondly, growth retarded babies

would have considerably less fat stores, increasing their specific

gravity and thus leading to underestimation of weight. That this may

be the case is suggested by the fact that Birnholz noted systematic

underestimation of fetal weight in the under-1000g group, for whom

he had to apply a recursive correction formula. In any case, the

claimed improvements in accuracy of their methods have not been

confirmed by independent workers.

Birnholz (1986) has suggested that , on the grounds of information

theory, averaging serial fetal weight estimates would improve the final

estimate, with the expected improvement being related to the square

root of the number of measurements. This makes the assumption that,

for a given individual, the error in fetal weight estimation on each

occasion is random, i.e. the signed error values are not correlated with

each other. This particular issue has not been reported on in the

literature.

All of the commonly used formulae place an emphasis on bony

landmarks and do not use any other soft tissue measurements apart

from the AC. While bony landmarks are accurate for the purposes of

estimating gestational age, the emphasis on these parameters could

explain their relative inaccuracy in estimating weight.

27

New weight estimation formulae should be explored that include

additional measures of soft tissue parameters. These were explored by

Favre and colleagues (1993), who reported better performance in the

small for dates group using the thigh circumference and femur length.

The standard deviation of the error was nevertheless fairly high at

15.9%, and they did not compare their formulae with older established

equations.

Other measures that should improve not only accuracy of fetal weight

estimation but also performance of other tasks include continual audit

and quality control, to ensure consistent techniques and peak

performance of equipment. Plastic ‘phantoms’ have been designed in

order check the technique and accuracy of the measurements

performed by ultrasonographers, but these are not in common use in

the UK.

The scope for making major errors in estimating growth velocity from

ultrasound fetal weight estimation has been pointed out in

correspondence by Gardosi (1994b). Substantial gains in accuracy will

be needed before abnormalities in growth velocity can be reliably

detected.

28

Table 3.1 Ultrasound fetal weight estimation formulae.

Authors Exponential formulae

Campbell & Wilkin

(1975)

wt=1000*exp(-4.564 +0.282*fac -0.00331*fac*fac)

Hadlock et al (1985) wt=exp(2.695 +0.253*fac -0.00275*fac*fac)

Hadlock et al (1985) log10(wt)=1.3598 +0.051*fac +0.1844*fl -0.0037*fac*fl

Hadlock et al (1985) log10(wt)= 1.4787 -0.003343*fac*fl +0.001837*bpd*bpd

+0.0458*fac +0.158*fac

Hadlock et al (1985) log10(wt)=1.3596 -0.00386*fac*fl +0.0064*hc

+0.00061*bpd*fac +0.0424*fac +0.174*fl

Shepard et al (1982) wt=1000*exp(-1.7492 +0.166*bpd +0.046*fac -

0.002646*fac*bpd)*(ln(10))

Warsof et al (1977) wt=1000*exp(2.302585*(-1.599 +0.144*bpd +0.032*fac

-0.000111*bpd*bpd*fac))

Persson et al (1986) wt=exp(ln(10)*(0.972*ln(bpd)/ln(10) +1.743*ln(ad)/ln(10)

+0.367*ln(fl)/ln(10) -2.646))

Balouet et al (1992) wt=0.1135exp(0.739*ln(fac) +1.179*ln(ethc) -0.041*ln(ithc))

Non-exponential formulae

Combs et al (1993) wt=0.23718*fac*fac*fl +0.03312*hc*hc*hc

Dudley et al (1990) wt=4.1*fl*apa +0.86*fl*hpa

Shinozuka (1987) wt=0.23966*fac*fac*fl +1.6230*bpd*bpd*bpd

Birnholz (1986) wt=(3.42928*bpd*ad*ad/1000) +41.218)

Birnholz (1986) wt=1.0206*{1.88496*[0.01*fl*ad+0.01667*bpd*ad +

0.01*bpd*bpd]*[(((-0.0069558*fl) +1.7394)*fl/10)

-3.3626]} -61.537

29

4. MODELS OF FETAL GROWTH

4.1 Introduction

Awareness of the limitations of birth weight standards as indicators of

fetal growth have led to the pursuit of ultrasound-defined intrauterine

growth standards. But while it is relatively easy to obtain birthweight

data from large populations, to derive ultrasound-defined standards

requires considerable effort in manpower, logistics and equipment.

Usually this data originates from ultrasound departments, and often

does not contain the clinical details of individual cases. As a result, all

of the fetal weight standards published to date have been derived from

relatively small samples.

The norms for commonly measured ultrasound parameters such as the

AC, FL and BPD are well established, yet relatively few studies

specifically address the issue of intrauterine weight gain. This is

partly because for the purposes of growth monitoring, most

ultrasound departments plot the individual measurements rather than

weight estimates. This in spite of several studies suggesting that the

EFW is at least as good as the AC for the detection of IUGR (Chang et

al, 1993; Hedriana & Moore,1994). Here we review the literature on

intrauterine weight curves, and describe an 'average' growth curve,

based on published data.

4.2 Comparative Analysis of Ultrasound-Derived Growth Curves

A total of seven studies describing intra-uterine weight gain were

retrieved .Studies on the growth of linear ultrasound parameters

without weight estimations were excluded, since derived fetal weight

curves can differ markedly depending on the weight equation being

used (see chapter 8 ). The characteristics of these studies are

30

summarised in table 4.1; these include population samples, weight

estimation formulae, mean birth weight and the methods of data

analysis. The fetal growth kinetics described by each study are

displayed in table 4.2. In order to describe the shape of the growth

curves independently of the predicted term weight, growth can be

expressed as a percentage of the predicted 280-day fetal weight, and

plotted as fractional growth curves. This allows comparisons in terms

of arbitrary descriptive landmarks such as gestation at which 50% of

the term weight is reached (G50), and the percentage of term weight

that is expected at 28, 37 and 42 weeks (P28, P37, P42). Figure 4.1

shows the medians of the ultrasound EFW curves plotted to 42 weeks

and also the corresponding birth weight data derived from the East

Midlands Obstetric Database (Wilcox et al, 1993a). Figure 4.2 shows

the derived fractional growth curves, as a percentage of term weight.

The equation for average fractional curve was obtained by taking the

arithmetic average of the coefficients of the derived growth functions

listed in table 4.1. This is plotted in figure 4.3; only a minimal degree

of deceleration is noted at term.

4.3. Alternative models of fetal growth

Rossavik and Deter (1986) proposed a sigmoid function to describe

fetal growth of any parameter, including weight. This function is of

the form:

P= c(t) k+st

where P is the ultrasound parameter, t is the duration of growth, k a

fixed coefficient determined by the anatomical characteristics, c and s

constants related to growth regulatory processes. This function allows

the prediction of individual 'normal' growth channels based on two

31

separate ultrasonic examinations before 27 weeks. This model was

applied prospectively by Simon et al (1989) to a number of parameters

including fetal weight. They found a small but significant systematic

error of overestimation for most of the parameters and fetal weight;

the standard deviation of the errors for fetal weight ranged from 6.7 %

to 9.4% , depending on gestation. This is well within the range of the

published errors of weight estimation formulae. The advantage of this

model is that reference charts are no longer needed; instead, growth

disturbances may be detected as deviations from the individually

projected standard. The main drawbacks are the need for two

ultrasound examinations before 27 weeks' gestation, spaced at least 5

weeks apart, and the need for appropriate computer equipment and

software to carry out complex calculations.

4.4 Discussion

Most of the differences between the published ultrasound growth

curves become apparent during the term period. They agree within a

100g band up to about 36 weeks gestation (figure 4.1). Beyond this

point, Jeanty's curve shows marked deceleration, whereas Deter's and

Otts display moderate acceleration. The remaining four curves

continue a linear trend evident from about 28 weeks. Jeanty's

abdominal circumference values are markedly below other standards

in late pregnancy, and this probably accounts for the deceleration in

his weight curve. The overall fractional average curve (figure 4.3)

shows only minimal deceleration at term, and this is in contrast to

birth weight standards based on menstrual data. Another approach to

deriving this curve would have been to use a weighted average; the

problem here is that two of the studies are cross-sectional. In any case,

the largest studies are included in the middle five curves, and thus it is

32

doubtful that a weighing procedure would change the shape of the

curve significantly.

On the basis of their published birth weight data, Ott's and Deter's

weight formulae overestimate fetal weight at term by 255g and 471 g

respectively, and this may account for the steeper slopes of their

curves. Larsen's study is the only one to use birth weight in order to

select the optimal growth model; yet even here there is a systematic

overestimation at term of about 170g. This contrasts with Hadlock's

study, of similar (cross-sectional) design, where an overestimation of

only about 20g was observed, probably because of better performance

of the weight estimation formula.

Of the five studies whose weight formula was not developed locally,

none compares more than 3 different weight formulas in order to

select the best. As will be discussed in chapter 8, the type of weight

equation selected may result in differences of more than 300g at term.

Hence, when producing standards of intrauterine weight gain,

measures should be taken to correct any systematic error due to the

weight estimation formula, since other centres will not necessarily

employ the same formula.

It is unlikely that the method of gestational dating makes a significant

contribution to the observed differences among these studies. This is

because, with the exception of Ott's study, menstrual dates were used

only if in close agreement with early ultrasound measurements; if they

did not agree, gestation age was estimated from the early ultrasound

measurements of the biparietal diameter .

The study by Larsen and colleagues is the only one to produce separate

standards for males and females; they describe a mean weight

difference between the sexes of 3.8%, but do not elaborate on whether

this holds true for all gestations or only for part of pregnancy.

It is apparent from figure 4.1 that all of the ultrasound derived medians

are higher than the birth weight data, by an average of about 100g.

33

Systematic weight estimation errors may account for some of this

difference, but other factors may be at play , since the differences

persist in the studies where this error was minimal, such as Hadlock's

or even negative, as in Jeanty's. The most likely explanation is that

infants born preterm are more likely to be growth retarded, and

preterm delivery in these cases is an escape mechanism from an

adverse intra-uterine environment.

Complex mathematical models such as Rossavik's, irrespective of

their validity, are unlikely to improve birth weight prediction in view

of the magnitude of ultrasound error. A recent study by Shields and

colleagues (1993) has shown that serial plotting of fetal measurements

on normal curves is as accurate in this respect as complicated

mathematical modelling. This would also be expected on the basis of

information theory (Birnholz, 1986).

34

Figure 4.1. Ultrasound-derived fetal growth standards compared with

Nottingham’s birth weight standard. The continuous curves represent the ultrasound-derived

standards published by Hadlock et al, Gallivan et al, Ott, Persson & Weldner, Deter et al, Larsen et al and

Jeanty (using Shepard’s weight formula). The values for the birth weight standard by Wilcox et al are

displayed as triangles. The middle four curves (Hadlock, Gallivan, Larsen and Persson) are closely

related.

35

Figure 4.2. Proportional growth curves for ultrasound-derived fetal growth

standards.

The ultrasound-derived standards published by Hadlock et al, Gallivan et al, Ott, Persson

& Weldner, Deter et al, Larsen et al and Jeanty (using Shepard’s weight formula) have

been transformed into ‘proportional’ growth curves, whereby the values for each gestation

represent the percentage of the predicted 280 day fetal weight.

36

Figure 4.3. Average proportional fetal growth curve.

The transformed proportional fetal growth curves of the ultrasound-derived standards published by Hadlock et al, Gallivan et al, Ott, Persson & Weldner, Deter et al, Larsen et al and Jeanty (using Shepard’s weight formula) have been averaged arithmetically to yield an average curve

.

37

5. SCREENING STRATEGIES FOR ABNORMAL FETAL

GROWTH

5.1 Introduction

In spite of the widespread introduction of obstetric ultrasound, our

clinical ability to detect the small-for-dates fetus remains poor, with

only about 30% -50% of cases detected in the ante-natal period (Jones,

1986; Hepburn & Rosenberg, 1986) . We are also rather ineffective in

detecting fetal macrosomia, with sensitivities of about 50% (Sandmire,

1993; Pollack et al, 1992).

The question has been raised on several occasions of whether

antenatal detection of growth disturbances is going to significantly

affect neonatal prognosis . While there is no long-term follow-up data

on this issue, there is some evidence that those SGA infants that are

detected tend to have a better short-term outcome than the undetected

cases (DeCourcy-Wheeler, personal communication). Hence we

should persist in our efforts to improve the antenatal detection of the

potentially compromised fetus.

There are two basic methods in practice for the detection of fetal

growth anomalies: obstetric ultrasound and assessment of the fundal

height.

5.2 Fetal Ultrasonography

Obstetric ultrasound has been investigated as a screening technique

since the early 70's (Campbell, 1971).While it has proved successful in

the detection of congenital abnormalities (Chitty et al, 1991) and in

establishing gestational age, the detection of growth restriction and

growth acceleration have remained much more elusive goals. Fetal

macrosomia is a common cause of concern for obstetricians, and it is

38

common practice to refer cases to the ultrasound department for fetal

weight estimation. There is, however, mounting evidence that so far

the performance of ultrasound in this weight group is poor compared

with other categories (Sandmire,1993). Two ultrasonic methods are in

common use for the detection and assessment of fetal growth

abnormalities: serial and static measurements of fetal anatomy .

At least five randomised trials have been performed since 1984 to

assess the efficacy of antenatal ultrasound as a screening tool for

growth restriction or developmental anomalies. These were published

by the following authors:

1. Bakketeig LS and collegues (1984)

2. Waldenstrom U and collegues (1988)

3. Ewigman B and collegues (1990)

4. Ewigman B and collegues (1993)

5. Newnham JP and collegues (1993)

The results have been somewhat contradictory and inconclusive. A

significant reduction in the number of SGA infants, a modest increase

in the mean birth weight and a significant reduction in the induction

rate was demonstrated by Waldenstrom and colleagues, following

routine scanning at 12 weeks. This was attributed to a reduction in

smoking due to visualisation the baby. On the other hand, in a group

undergoing both Doppler and ultrasound imaging on up to 5

occasions, Newnham and colleagues noted a slight but significant

increase in the SGA frequency in the screened group. In the largest

randomised study to date (4) involving 15151 low-risk pregnancies ,

no significant differences in outcome were noted between the screened

and the routine management group. The latter, however, did undergo

ultrasound examination when clinically indicated, thus limiting the

scope of the inferences that can be made from this study.

39

Optimal strategy for the detection of growth restricted fetus continues

to be a controversial issue (Daniellan and colleagues,1994). The study

by Chang and colleagues (1993) suggests that the use of either growth

velocity or single fetal weight estimates is rather limited at detecting

the truly growth restricted fetus as defined by neonatal morphometric

indices; at a false positive rate of 10% only 20-40% of the growth

retarded fetuses were detected. Similar figures were reported by

Daniellan and colleagues (1993). Both of these studies used

morphometric indices as the definitive criteria for IUGR; the

limitations of these indices are discussed in chapter 9. The data

presented by Hedriana and Moore (1994) suggests that a single

ultrasound examination is nearly as good as multiple examinations in

predicting the birth weight, but they did not test the hypothesis that

growth velocity as assessed by multiple measurements is a better

predictor of poor outcome than fetal weight estimated from a single

measurement.

5.3 Fundal height assessment

5.3.1 Introduction

The earliest report on measuring the symphysial-fundal height (SFH)

was published in the German literature by Spiegelberg in 1891.

Rumboltz and McGoogan (1953) were the first to describe a

relationship between reduced growth of the uterine fundus and

'placental insufficiency'. Since then, many conflicting reports have

been published on screening for growth disturbances by the clinical

measurement of the symphysial-fundal height (SFH).

5.3.2 Precision and accuracy of SFH measurements.

The estimation of symphysis-fundus distance is subject to

considerable error. Bagger and colleagues (1985) reported an average

40

intra-observer variation of 1.5-2 cm and an inter-observer variation of

4 cm; these were not correlated with the actual SFH measurements.

Some observers were found to consistently overestimate or

underestimate .The accuracy of SFH measurements was checked by

comparing clinical measurements with those obtained by ultrasound

guided measurement; the differences between the former and the latter

ranged from -0.2 to +2.7 cm. Calvert (1982) found intra-observer and

inter-observer coefficients of variation of 4.6% and 6.4%, slightly

lower values than those published by Bagger's group. The limits of

agreement of the inter-observer variation were estimated in a study by

Bailey and colleagues (1989) to be -5.0 to +1.6 cm, corresponding to a

coefficient of variation of 4%. This study highlights what is probably

the major shortcoming of SFH measurents: that the error due to inter-

observer variation, even between experienced practitioners, is too

wide in relation to the standard deviations in the published reference

charts.

5.3.3 Fetal weight estimation by fundal height measurement.

Estimation of fetal weight by unaided clinical palpation was reported

by Loeffler (1967) to be accurate within 450g of the birth weight in

80% of cases; it is of interest that in this study the accuracy of the

individual observers improved with experience.

The first attempt to estimate fetal weight by measuring the fundal

height was reported by Johnson and colleagues (1954). This method

included correction factors for engagement of the fetal head and

obesity.The standard deviation of the reported errors was 353g, which

is slightly greater than ultrasound estimation using Campbell's formula

for abdominal circumference(Campbell & Wilkin,1975). More

recently, a Belgian study of an African population showed that SFH

was more closely related to fetal weight than gestation (De Muylder

and colleagues,1988).

41

5.3.4 Derivation of SFH standards.

In all the studies, women without 'sure' dates or an early dating

ultrasound scan were excluded. In six of the nine papers reviewed the

data was filtered by removing those cases whose birth weights were

outside arbitrary limits; depending on the degree of restriction, the

standard deviation of the SFH values thus obtained would be

narrower. The inclusion criteria are summarised in table 3. In all of the

above studies there appears to be some flattening of the SFH curve

near term. As pointed out by Westin, miscalculation of gestational age

may lead to serious error. In all the standards so far published,

gestation was reckoned on basis of menstrual dates, ultrasound dating

being used routinely to 'confirm' dates (Pearce),or reserved for those

cases whose last menstrual period was unknown (Calvert, Quaranta)

or otherwise excluding those cases without a known LMP (others).

Geirsson (1991) has convincingly argued that even when certain, LMP

dates are less reliable than those derived from ultrasound, with an

overall tendency to overestimate gestation. He pointed out that birth

weight standards in populations whose gestations are derived from

ultrasound dating show a much less marked 'terminal flattening' of the

reference curves at term. This has also been our experience (Wilcox et

al, 1993a). It is likely that SFH reference standards are also subject to

the same effect.

5.3.5 Ethnic variations in SFH standards.

Table 5.1 shows the differences in SFH standards by ethnic group.

For comparative purposes, the 40-week median value is given for each

group; the SD deviation is omitted because of the widely different

methodologies used. It thus appears that for European populations

there are only small differences among the published standards. Indian

42

populations, however, have lower term values of around 33 cm,

compared with 36 cm for Europeans. Grover and colleagues (1991)

published a reference standard derived from 200 low-risk Indian

women with birth weights within +/- 1 SD of the local standard.

Compared with European curves, their fundal height increments were

similar from 20 weeks until 32 weeks (1 cm/week); some slowing was

noted thereafter, resulting in term values that were 3 -4 cm lower.

Similar values were published by Mathai and colleagues (1987) in

South India. However, Depares and colleagues (1989),on comparing

European and Pakistani SFH values in Bradford (UK), could not

detect significant differences.This may be because of the small

samples in their study.Oguranti studied SFH in 581 unselected

Nigerian women; their values were also lower than European

standards pre-term, but reached similar values at term. These

differences in SFH standards among ethnic groups may arise from the

well-known differences in birth weights, but could also be due to other

factors such as maternal body build and prevalence of fetal pathology.

5.3.6 Clinical performance of SFH measurements.

The definitions of 'positive for SGA' by SFH measurements differ in

the literature. The populations tested also differ, some being high-risk,

hence artificially increasing the detection rate. In most cases at least

two or three consecutive readings have to be below the 10th centile.

Theoretically, increasing the number of abnormal measurements in

order to diagnose SGA should reduce the false positive rate. In a large

uncontrolled study of low risk, uncomplicated pregnancies Westin

(1977) in Sweden showed that SFH measurements were superior to

maternal weight gain, maternal girth measurements, and biochemical

analytes (uE3, HPL) for the detection of the SGA infant.The routine

introduction of reference SFH charts in the case notes of all their

patients was associated with a significantly steeper fall in the local

43

perinatal mortality rate compared with the overall Swedish statistics.

Pearce and Campbell (1987) compared serial SFH mesurements with a

single fetal abdominal circumference (FAC) obtained by ultrasound as

screening tests for SGA. No significant differences were noted

between the two, when specificities were set equal at 79%.

Interestingly, they found a peak sensitivity at 34 weeks, similar to

Quaranta's peak at 32 weeks. The only randomized controlled trial on

the clinical performance of SFH measurement versus clinical

palpation was reported by Lindhard and colleagues (1990), in a

population of 1639 women. No significant differences were found

between the two methods in terms of the detection rate of SGA,

number of interventions, additional diagnostic procedures or the

condition of the newborn.

Table 5.3 summarises the clinical performance of SFH measurements

in detecting SGA infants. Because fundal height standards and the

definitions of an abnormal SFH test vary, it is not possible to pool

results in order to arrive at average values.Persson and colleagues

(1986) summarise positive predictive values for various studies

including their own, which is the largest. They range from 13% to

79%; there is a tendency for larger studies to show lower PPV's. This

is consistent with the hypothesis that the larger the number of

observers, the greater is the effect of inter-observer variability and

hence the poorer the tests' performance.

5.4 Discussion

If , as one would expect, antenatal detection of IUGR improves

neonatal outcome, then an effective screening strategy for growth

disturbances is a major target in perinatal medicine. That randomised

studies have not been able to document a definite improvement in

outcome following routine ultrasound examinations may be due to a

number of factors. To some extent this is likely to reflect the limited

44

accuracy of ultrasound in estimating fetal size; in none of the studies

was fetal weight rather than the individual biometric parameters

plotted. Another factor is the threshold for clinical intervention. In

Newnham's study, the induction rates of the screened women did not

differ significantly from the regular group even though the former

were significantly more likely to be given the diagnosis of IUGR. This

suggests some reluctance on the part of the clinicians to act on the

basis of the ultrasound findings. Not to be discounted is the lack of an

appropriate standard for detecting deviations in growth velocity.

The concept that fetal growth could be monitored by such a simple

and inexpensive tool as a tape measure has generated wide

interest.The lack of agreement in the literature on the efficacy of SFH

measurements is not surprising, given the wide differences in

definitions and population sampling. The fact that the median 40-week

values for different ethnic groups reflect their differences in mean

birth weights provides additional support to the notion that SFH

measurements are an indicator of fetal size.

At least three studies compared traditional clinical palpation with SFH

measurements for the detection of SGA fetuses. Secher and colleagues

(1990) found no significant differences betweeen these two methods.

Similar results were obtained by Pschera and colleagues (1984), and

by Lindhard and colleagues (1990). This may be due to the clinicians’

longer experience with clinical palpation as opposed to the newer SFH

measurement, and hence the results may have been biased by this

factor.

There is no good evidence that introduction of routine SFH

measurements leads to a reduction in perinatal mortality rates. The

improved figures reported by Westin may well have been a chance

result, since this study was not properly controlled.

In view of the magnitude of the error due to inter-observer variability,

it is likely that SFH measurements are clinically more useful when

45

performed serially and frequently by the same observer using a

consistent technique.

There is some evidence to suggest that test performance for SFH may

be optimal around 32-34 weeks, and it is of interest that this coincides

with the period of peak performance for ultrasonic fetal weight

estimation of 32 to 36 weeks (Hedriana & Moore, 1994). If ultrasound

growth screening is to be performed as a one-stage routine, then this

gestational age interval offers the best hope for success. It could also

be possible to improve the accuracy of ultrasonic fetal weight estimate

by combining it with the SFH; this approach was described by Farmer

and colleagues (1992), who, in addition toultrasound data and the SFH

also included maternal characteristics such as height and parity. They

developed a trained neural network which, in the case of suspected

macrosomia, was significantly more accurate in estimating fetal

weight than either Hadlock’s or Shepard’s formula; its mean

percentage error was 4.7% with a standard deviation of 3.9%.

Accurate fetal weight estimation is the key to an effective screening

program for growth disturbances. This is an area that continues to

evolve, and improvements may be brought about by advanced

information processing techniques, using current clinical

measurements.

46

Table 5.1 Criteria for Population Selection of SFH Standards

Study Population selection criteria for

derivation of standard.

Quaranta Birth weight between 25th and 90th

centiles

Belizan Birth weight between 10th and 90th

centiles

Westin Mean birth weight +/- 1 SD

Calvert Birth weight between 10th and 90th

centiles

Pearce Birth weight between 10th and 90th

centiles

Grover Birth weight within mean +/- 1sd

Mathai Term delivery of live infant

Rosenberg Birth weight between 25th and 90th

centiles

Ogunranti All patients sure of their dates.

Persson Infant weight/length ratio between 10th

and 90th centile

47

Table 5.2 Ethnic variation in SFH standards.

Study Ethnic Group Sample size 40-week value

Quaranta European 138 36.5

Belizan Latin American 139 34.5

Westin European 428 36

Calvert European 381 36

Pearce European 699 37

Persson European 1350 36

Ogunranti Afro-caribbean 581 39.4

Grover Indian 200 33

Mathai Indian 250 33.8

48

Table 5.3 Clinical performance of SFH measurements.

Study Definition of abnormal result FP sens spec

Quaranta 2 cons or 3 isolated vals <10th cent 21 73 80

Belizan 1 single val <10th 21 86 90

Westin 1 single val<2cm below median or

3 cons static or decreasing vals

54

75

64

Lindhard As above 41 28 97

Calvert 1 single val<2cm below median or

3 cons static or decreasing vals

80

76

60

Pearce 1 single val below 10th centile 64 76 79

Grover 1 single val < 1sd below mean 16 81 94

Mathai 1 single val < 1sd below mean 23 78 88

Rosenberg 20% of measurements below

10th centile

21

62

85

Cnattingius 'catch-up and low' SFH growth NA 79 92

Persson outside 2sd's NA 27 88

49

6. PRINCIPLES OF THE CUSTOMISED GROWTH CHART.

6.1 Introduction

To produce an adjustable standard that takes into account the

physiological factors influencing fetal weight is a computational task

that is not easily performed by using tables and graphs. The logical

solution to this problem is computer software.

The principles of a computer-generated growth standard which carries

out this task were first published in the Lancet in 1992 by J.Gardosi,

Professor A.Chang and colleagues.Unlike previous attempts that relied

on fixed correction factors from tables applied to birth weight

standards, the calculations were performed by computer software and

growth charts could be displayed on screen and printed. Ultrasound-

derived fetal growth standards rather than birthweight standards were

used for generating growth curves, and corrections factors that

included maternal weight at booking, maternal height, parity, ethnic

group and fetal sex were scaled up or down depending on the

gestational age.

6.2 The prediction of normal growth potential

The initial obstetric database consisted of 4179 pregnancies with

ultrasound-confirmed dates. Multiple regression analysis showed that

in addition to gestation and sex, maternal weight at booking, height ,

ethnic group and parity were factors that significantly affected birth

weight. This was confirmed by analysis of variance.The multiple

regression analysis was repeated by Mr Mark Wilcox on a much larger

sample of 38114 cases, smoking being entered as an independent

variable.Continuous variables such as gestation, height and weight

were centered around their means so as to minimize computational

50

problems. The details of the analysis have been published elsewhere

(Gardosi et al, 1994), and the coefficients are given in table 3.1. This

regression model allows us to estimate the fetal weight at 40 weeks for

any combination of maternal characteristics. In the prediction of

normal birthweight, the confounding effect of smoking is dealt with by

entering the non-smoking coefficient for all cases.The model can only

explain 31% of the variability of birth weight in the database, but this

is likely to be an underestimate, since up to 55% of records in

obstetric databases may have at least one error (Dombrowski, 1994);

the East Midlands Obstetric Database is not subject to rigorous quality

controls.

6.3 The generation of normal fetal growth curves

The method of generating growth curves relies on the working

hypothesis that, for normally grown fetuses, the morphology of the

growth curves is approximately the same irrespective of birth weight.

This means that if the mean curves from population subgroups are

described in terms of a polynomial function of gestation, division of

the polynomial coefficients by the 40-week weight will yield a new

function whose coefficients will not vary appreciably between

subgroups. Some indirect support for this postulate comes from the

work by Dunn (1989) and Thomson (1968).

In chapter 4 we reviewed the literature on ultrasound-derived fetal

weight growth curves and for each mean curve we derived a

'proportional' curve, by dividing the coefficients of the original by its

predicted 40 week weight . An average growth curve was produced by

taking the arithmetic mean of the respective coefficients; this function

will estimate the percentage of term weight for any gestation.

Multiplying this function by the predicted 40-week weight obtained

from the regression model will yield an individual 'ideal' antenatal

growth curve. The 10th and 90th centile reference curves are derived

51

from the standard error of the regression analysis, and are adjusted at

each gestation so that the ratio of the standard error to fetal weight

(coefficient of variation) remains constant at 11%.

Some paired examples of the charts are shown in figures 6.1 to 6.4.

Mrs Average (fig 6.1) is a European woman of average height and

weight (163 cm and 63.4 Kg) who has had a previous delivery of a

male infant weighing 2700 g at 37 weeks. The value within the square

(9) is the centile value of this weight for 37 weeks. The expected birth

weight at 40 weeks is just over 3800 grams. Figure 6.2 shows a

woman of the same parity and size but of Indo-Pakistani origins; the

term birth weight expectation is reduced to 3600 grams, but the

previous delivery is not classified as SGA (centile 19). The chart of a

large European lady with the same obstetric history is shown in figure

6.3; the previous birth weight is given a centile value of 4. In contrast,

a short and light lady with the same history would be given a centile

value of 24 (figure 6.4).

6.4 Other functions

The early versions of the customised growth charts also allowed the

entry of fundal height measurements by including a fundal-height y-

axis on the right side. This was calibrated to approximate the standard

published by Pearce and Campbell (1987).

An axis for the fetal abdominal circumference was also displayed,

based on the standard of Deter and colleagues (1982). Previous

deliveries and their birth weight centiles may be entered and displayed

on the same chart.

The x-axis displays gestation as exact weeks and also the calculated

corresponding dates.

The expected date of delivery, maternal height and weight, parity and

ethnic origin are displayed on the top left hand corner of the chart. In

the latest version of the chart, the maternal body mass index is

52

displayed if this falls below the 10th centile for our pregnant

population at booking, as this indicates the possibility of malnutrition

in the periconceptional period.

6.5 Clinical performance

The initial sample of 4179 deliveries contained 385 cases with a birth

weight below the 10th centile by unadjusted criteria (SGA). Of these,

only 278 were still below the 10th centile following adjustment for

maternal characteristics. Hence 107 (28%) would have been given a

false positive diagnosis of SGA by conventional standard. Adjustment

90 cases that would have been missed by conventional assessment.

Babies that by the conventional standard only were deemed SGA had

significantly fewer instances of low Apgar scores.

6.6 Discussion

It is apparent from the foregoing that this method of producing an

adjustable growth standard relies on many hypotheses on the

physiology of fetal growth. Yet these are necessary if a model is to be

developed. These may be summarised as follows:

1. The physiological variables affecting fetal weight at term are also

effective in the antenatal period in proportion to the fetal weight.

2. The intrinsic shape of the normal fetal growth curve is the same for

all subgroups, differing only by a scalar, or 'magnification' factor

proportional to the predicted term weight. This postulate will be

referred to as the ‘proportionality ‘ principle.

3. The average fetal growth curve entered in the program is close to

the true population average.

53

4. The distribution of fetal weights is approximately normal for all

gestations.

5. The variance of fetal weights is a constant fraction of the gestational

median , ie a constant coefficient of variation of 11% (derived from

term birth weights).

6. The selected variables of maternal weight, height, parity and ethnic

group have mainly a physiological rather than pathological

significance.

A major problem is to separate physiological from pathological effects

e.g. to what extent is a low maternal weight at booking due to

nutritional factors as opposed to constitutional factors.

In view of the known adverse effects of malnutrition in early

pregnancy on fetal growth, measures are needed to prevent the

application of unduly small adjustments for maternal weight in cases

where the low values are due to undernutrition at booking. While this

is an infrequent problem in western populations, this is not the case in

the developing world. To deal with this issue, the current version of

the customised growth chart calculates the body mass index (BMI) at

booking; if this is below the 10th centile the maternal weight at

booking is corrected so that the BMI is at the 10th centile. The

birthweight expectation is thus the one for a normally nourished

individual at the lower end of the normal range. A similar algorithm is

applied at the 90th centile of the BMI.

The rationale for making adjustments on the basis of parity is an issue

open to debate. The primigravid state, although 'natural', would be a

relatively infrequent finding in a female population of reproductive

age unaffected by contraceptive practices, as the statistics from a

54

century ago show. Goldenberg and colleagues (1989) stated that the

genetic potential for fetal growth in primigravidas and multigravidas is

identical and that the differences noted between these two groups are

attributable to growth-restricting factors operating in primigravidas.

Henced they advocated a parity-independent standard derived from a

population of mixed parity. On the other hand, Thomson and

colleagues (1968) argued in favour of adjusting for parity, believing

that the effect of parity is a physiological factor present in the fetal

environment.

The effects of smoking on birthweight are relatively large. An

alternative method to obtaining regression coefficients applicable to

the whole population would be to include non-smokers only. But this

process could theoretically lead to the selection of a genetically

'supra-normal' population, and thus adjustment factors that may not be

universally applicable.

More robust estimates of the coefficients for the different ethnic

groups would have required a much larger sample . We did not have

sufficient numbers of Far Eastern women to separate them from the

heterogenous 'others' grouping, and hence we do not have a reliable

coefficient for these.

In view of the known pathological effects of malnutrition on fetal

growth, measures are needed to prevent the application of unduly

small adjustments for maternal weight in cases where the low values

are due to undernutrition at booking. While this is an infrequent

problem in western populations, this is not the case in the developing

world.

In the current version of the customised growth program, a lower limit

is entered for the weight adjustment. If the maternal booking weight is

below this limit, the adjustment does not decrease; the birthweight

expectation is thus the one for a normally nourished individual at the

lower end of the normal range.

55

Large databases containing maternal characteristics, accurate antenatal

fetal weight estimates and birth weights would be needed to address

these issues.

56

Table 6.1 Multiple regression coefficients for the prediction of normal growth potential . CONSTANT 3409.853 STANDARD ERROR 389.032 ADJUSTED R SQUARE 0.31824 NUMBER OF CASES 38114 Coefficient GESTATION (from 280 days) Gest 1 20.667 Gest 2 -0.21289 Gest 3 -0.000167 SEX Male

- 116.871

Female -233.742 MATERNAL HEIGHT (from 162 cm)

7.764

BOOKING WEIGHT (from 64.3 kg)

Weight 1 8.676 Weight 2 -0.11740 Weight 3 0.000716 ETHNIC GROUP European 31.670 Indian Sub-cont. -154.263 Afro-Caribbean -95.789 Other -33.446 PARITY Para 0 4.898 Para 1 112.904 Para 2 153.458 Para 3 154.767 Para 4 154.690 SMOKING Non-smoker 31.9160 Smokes 1-10 -120.602 11-20 -182.568 > 20 -214.112

57

7. METHODS OF GESTATIONAL AGE ESTIMATION

7.1 Introduction

An accurate assessment of gestation is crucial in the development of

fetal growth standards. It is also of importance in evaluating most of

the variables in current use for fetal monitoring and in the

management of post-term pregnancy.

Gestational age may be estimated ultrasonically by measuring fetal

parameters such as the crown-rump length (Robinson & Fleming,

1975) until 12 weeks and the biparietal diameter (Campbell &

Newman, 1971) until about 20 weeks gestation. Measurement of the

BPD in the mid-trimester has been shown to be 5% more accurate in

predicting the date of delivery than impeccable dates (Pearce and

Campbell,1983).

In practice, most ultrasound departments follow either the '10-day rule'

or the '7-day rule', whereby preference is given to menstrual dates if

these are within 7 or 10 days of the ultrasound estimate by the BPD.

Here we study the reliability of these methods by analysing the East

Midlands Obstetric Database.

7.2 Materials and Methods

The computerised obstetric records of three major maternity units in

the East Midlands (City and University Hospitals in Nottingham and

Derby City Hospital) date from 1986. So far more than 60000 cases

are available for analysis. A significant drawback is that the database

does not allow the reliable exclusion of induced labour. Multiple

pregnancies, stillbirths, congenitally abnormal babies, late bookers

(over 24 weeks), preterm deliveries (<37 weeks) and those with

unknown menstrual dates were excluded. A total of 31747 cases with

both menstrual and ultrasound data were retrieved for analysis.

Gestational age at delivery was calculated in days from 1. The

58

biparietal diameter according to the dating charts by Campbell (1971)

if over 13 weeks at booking, otherwise the crown-rump length was

used (Robinson & Fleming,1975) and 2. The last menstrual period.

The error in predicting the EDD was calculated in days for three

different dating methods: ultrasound data only, the 7-day rule and the

10-day rule as follows:

Error (days) = estimated gestational age at delivery - 280

These were expressed as signed and as absolute values. Statistical

analyses were performed with the 'SPSS for Windows' statistical

package.

7.3 Results

Table 7.1 shows the means, standard deviations and skewness of the

errors for each method. The distribution of the error is weakly but

significantly skewed to the left. Analysis of the signed errors suggests

that ultrasound on its own and the 7-day rule tend to underestimate the

EDD, whereas the 10-day rule overestimates it. Because of the

significant skewness of the data, the differences between the methods

were evaluated non-parametrically using Wilcoxon Matched -Pairs-

Signed-Ranks Test; the results are shown in table 7.2 . The mean and

the standard deviation of the absolute error using ultrasound alone is

slightly smaller than the other methods. Dating by ultrasound only is

significantly more accurate in predicting the EDD than either the 7-

day or 10-day rules.

7.4 Discussion

The length of gestation has traditionally been calculated from the first

day of the last menstrual period using Naegele's rule -i.e., by adding 7

days and 9 months to the date of the last menstrual period. Although

this formula has been attributed to Franz Karl Naegele (1778-1851), it

was first proposed by professor Herman Boerhaave (1709) at the

59

University of Leyden (Speert, 1958). The formula implies a mean

duration of pregnancy of 274 days from the LMP, which is at variance

with the observed value of 280 days (Doring,1962). If the dates were

not 'reliable', gestation was estimated by clinical palpation. There is

mounting evidence that even among women with 'reliable' menstrual

dates, considerable error may arise in the calculation of gestation. This

is because the onset of ovulation within the menstrual cycle is

relatively erratic, particularly in younger women (Geirsson,1991), and

may also vary from cycle to cycle. We have studied the error of

menstrual dates using the BPD-derived gestation as the reference

standard, in a database of 31561 cases (Gardosi & Mongelli, 1993).

For 21.5% of pregnancies ultrasound scan dates were outside plus or

minus 7 days from the dates based on menstrual dates. Furthermore,

the distribution of the error associated with menstrual dates was

significantly skewed, such that the 95% confidence interval for

gestational age derived from menstrual history was -27 to +9 days.

In our unit the ultrasound department calculates the EDD at booking

by using the 10-day rule if reliable menstrual dates are available,

otherwise the BPD or the FL is employed. About 6% of all cases are

induced for post-maturity on this basis, but these cannot be identified

reliably from the computerised records. Therefore one would expect

that this iatrogenic interference on the normal duration of pregnancy

would bias the statistics in favour of the 10-day rule. However, in spite

of this bias, we found that dating by ultrasound alone appears the best

method for predicting the EDD; bearing in mind the direction of the

bias, it is likely that the advantage of using ultrasound only may be

greater than what our figures indicate.

It has been suggested that the use of the BPD alone in estimating

gestational age is flawed, in that the larger babies would be assigned a

longer gestation than the smaller babies (Henriksen & Wilcox, 1994).

Persson and colleagues (1978) did find a relationship between birth

60

weight centiles and the size of the BPD in early pregnancy, but the

difference between the small (<10th centile) and large (>90th centile)

babies amounted to only about 1.5 mm at 18 weeks. This difference is

equivalent to less than one day's variation in gestational age by

Campbell's dating standard. We recently investigated this issue using a

database of 19 singleton pregnancies precisely dated from in-vitro

fertilisation or artificial insemination. No significant correlation was

found between the BPD centiles at the booking ultrasound

examination and the birthweight centiles (Gardosi et al, 1994).

In this study we assumed that the modal length of normal pregnancy of

280 days is equally applicable for all population subgroups. Only a

limited number of studies have been published on this issue, all of

them using menstrual dates. They report trivial differences in duration

of pregnancy between social classes or ethnic groups (Butler &

Bonham, 1963; Henderson, 1967). There is also some evidence that

BPD standards do not vary appreciably between ethnic groups (Vialet

et al, 1988; Simmons et al, 1985), and thus this should not be an

important source of bias.

Our findings do not support the use of the 7-day or 10-day rule in the

assignment of gestational age when valid ultrasound measurements are

available. Although the error associated with their use may not matter

in clinical practice (Mongelli & Gardosi, 1994), these methods are

best avoided in defining normal standards in pregnancy.

61

Table 7.1 Descriptive statistics for the errors resulting from each

dating method.Values expressed in days (N = 31747).

Variable Mean

SD Skew S.E.

Skew

Min Max

Signed Errors

Ultrasound

only

-0.72 8.50 -0.12 0.01 -20 28

7-day rule -0.20 8.65 -0.15 0.01 -27 28

10-day rule 0.18 8.79 -0.16 0.01 -30 30

Absolute Errors

Ultrasound

only

6.91 5.00 0.69 0.01 0 28

7-day rule 7.02 5.06 0.70 0.01 0 28

10-day rule 7.15 5.12 0.73 0.01 0 30

62

Table 7.2 Significance of differences in absolute errors between

dating methods. Wilcoxon Matched -Pairs Signed Ranks Test.

Pairs: A Vs B Mean

Rank

A>B A<B Ties

A=B

Z-value 2-tailed P

value

Ultrasound only

Vs 7-day rule

8443.96

8782.91

8251

8989 14507 -7.1 <0.00005

Ultrasound only

Vs 10-day rule

9493.64

10137.31

9231 10438 12078 -11.4 <0.00005

10-day rule

Vs 7-day rule

1265.5

1140.34

1449 980 29318 -10.4 <0.00005

63

8. FORWARD PROJECTION OF FETAL WEIGHT ESTIMATE

The proportionality principle applied to dynamic fetal weight

estimation.

8.1 Introduction.

Fetal weight estimation from ultrasound parameters or other methods

is almost invariably made from static measurements. In practice,

delivery often does not occur until days or weeks after the ultrasound

examination, and the weight estimation is not corrected for this time

interval. This problem was appreciated by Spinnato and colleagues

(1988) who published a series of dynamic weight estimation formulae

to allow forward projection of the fetal weight estimate to the

expected date of delivery.

Here we present an alternative method that, in addition to forward

weight projection, should also allow for backward fetal weight

interpolation from the birth weight.

8.2 Subjects and Methods.

In order to compare our method with Spinnato’s method, we selected

only those cases who delivered within 35 days of the last ultrasound

examination. Two hundred forty two cases were available for analysis;

these were liveborn infants, inclusive of adverse outcomes and

congenital malformations. The forward projection equation was

derived from the principle that the shape of the fetal growth curve is

similar for all cases irrespective of the birth weight. It is in fact a form

of proportional extrapolation, which we will refer to as the ‘prop-ex’

method. Hadlock’s growth formula was selected because: 1. The

weight estimation formulae considered were also developed by

Hadlock and colleagues and 2. This growth formula is close to the

average of previously published growth formulae (Gardosi et al,

1994). If Hadlock is the growth function, EFW the ultrasound fetal

64

weight estimation, u is the gestational age at ultrasound examination

and d is the gestational age at delivery, the following relationship

applies:

EFWu : EFWd = Hadlock(u) : Hadlock(d)

Therefore:

Projected fetal weight at delivery:

EFWd = EFWu * Hadlock(d)/ Hadlock(u)

Likewise, Spinnato’s equivalent dynamic weight equation for the

Hadlock weight formulae is:

Log (EFWd) = 1.0009 * log (EFWu ) +0.0043(d -u)

Both techniques were applied to our sample. Prediction errors were

calculated both as signed and as absolute percentage errors of the birth

weight (BWT) as follows:

Percentage error = 100* (EFWd -BWT)/BWT

Trends in the error in relationship to birth weight were examined using

Spearman’s rank correlation coefficient. Differences between the two

methods were tested by Wilcoxon’s matched pairs signed-ranks test.

8.3 Results

The signed and absolute percentage errors for the two methods are

shown in Table 8.1. Table 8.2 displays the statistical significance of

the differences between these two methods.

The errors from using Spinnato’s method were not significantly

correlated with true weight ( R = -0.0880, P>0.05), whereas with our

65

method there was a weak but statistically significant inverse

correlation with the true weight (R= -0.23, P<0.0005). This correlation

was not significant if birth weights below 3200g were excluded.

For both techniques, there was no significant correlation between

either the signed or absolute errors and the lag-time interval.

8.4 Discussion

These statistics show that the proportional extrapolation method we

describe for birth weight prediction from remote ultrasonographic

examination is significantly better than Spinnato’s technique in our

population. This may be because the latter uses the lag-time difference

between ultrasound examination and delivery, without considering the

actual values of the gestational ages of these events.

The random errors described in Spinnato’s original paper are slightly

lower than when his method was applied to our population (11 vs 12

%), with a tendency towards underestimation as opposed to

overestimation in our sample.

Two other advantages of the prop-ex technique are that: 1. It is

applicable to any fetal weight estimation technique, whereas

Spinnato’s method requires different equations for different weight

estimating formulae. 2. It can be modified to allow for retrospective

fetal weight estimation from the birth weight by interpolation as

follows:

IFWu = BWT* Hadlock(u)/ Hadlock(d)

where IFWu is the interpolated fetal weight. This formula, however,

cannot be validated without an independent, highly accurate method of

fetal weight estimation such as NMR.

The results of these studies validate the concept of incorporating a

lapse-time factor in equations to predict the birth weight from remote

66

ultrasonographic data. As the errors are not correlated with the lag-

time interval, both methods are accurate throughout the range of time

intervals measured. It is likely that accuracy will be lost in cases

affected by growth disturbances, and this is reflected in the negative

correlation between the signed errors and the birth weight, which is

lost for birth weights over 3200 grams.

These findings also support the ‘proportionality’principle - a key

aspect of the customised growth chart program- which is the

assumption that fetal growth in normal populations is essentially the

same, once growth is expressed in terms independent of the actual

birth weight.

67

Table 8.1.Signed and absolute percentage errors for projected fetal weight estimation. Formula Systematic Error (%) Random Error (%) Prop-ex 6.01 11.0 Spinnato 8.56 12.0 Mean Absolute Error (%) Standard Deviation (%) Prop-ex 9.9 7.7 Spinnato 11.8 8.9 Table 8.2. Non-parametric comparison of the errors generated using the two methods on the same population. Wilcoxon Matched-Pairs Signed-Ranks Test.

Differences in Ranks Mean Rank No Cases

- Ranks (Spinnato < Prop-ex) 172.70 51 + Ranks (Spinnato > Prop-ex) 130.75 225 Ties (Spinnato = Prop-ex) 1

Total = 277

Z = -7.7646 2-Tailed P-value <0 .00005

68

9. SELECTION OF ULTRASONIC W EIGHT FORMULA

9.1 Introduction

The performance of ultrasound fetal weight estimation formulae may

have important implications for growth standards and antenatal

screening. In choosing the best formula, two main selection criteria

need to be satisfied:

(a) Minimal overall error.

(b) Minimal bias in the error in relation to fetal weight.

The first condition is self-evident. The significance of minimising

error trends in relation to fetal size has been somewhat underestimated

in the literature. If a particular formula has a strong tendency to

overestimate the small and underestimate the large fetus, both the

weight standard and screening performance may be adversely affected.

One may argue that fetal growth standards should be weight formula-

specific.If this was the case, then centres where a particular formula is

either not in use or is not suitable will not be able to use the standard.

In this study we examine the effect of different weight estimation

formulae on apparent growth kinetics, and their clinical performance

in our population is evaluated .

9.2 Patients and Methods

Persson and colleagues (1986) have shown that the average fetal

weight curve derived from the means of the ultrasound parameters for

each gestation is very similar to that derived from the means of the

individual weights. Hence, to illustrate the effect of different weight

formulae on growth curves, we studied the means published by Chitty

and Altman (1993) for individual ultrasound parameters. These were

input for the following weight estimation formulae:

- Hadlock (BPD, AC, FL)

69

- Hadlock (AC, FL)

- Warsof (BPD, AC)

- Shepard (BPD, AC)

- Campbell (AC)

- Modified Persson (BPD, AC, FL)

Persson's original formula used the abdominal diameter (AD); this was

modified by converting the AD into AC as follows:

log10 EFW (grams) =

0.972* log10 BPD + log10 (AC/3.1416) +0.367*log10 FL -2.646

The calculations were performed by computer software, and the

different growth curves for each formula displayed graphically using

Cricket Graph software.

In order to determine the best formula for the population in our study,

we selected those cases that delivered within 14 days of an ultrasound

examination. A sample of 129 cases was retrieved, from a total of 171

cases that had been seen one year before the completion of the growth

study. A correction for the interval growth between time of ultrasound

examination and delivery was made by projecting forward the

estimated fetal weight using Hadlock's fetal growth formula as

follows:

Predicted weight =

EFW * H(gestation at delivery)/H(gestation at scan)

where EFW is the ultrasound-estimated fetal weight and H is

Hadlock's fetal growth function as described in chapters 4 and 8.

Using the weight formulae listed above, the signed percentage error

for each case was computed thus:

Percentage error = (Predicted weight - Birth weight)/ Birth weight

70

A positive value indicates overestimation whereas a negative value

indicates underestimation of true fetal weight. The weight formulae

were then corrected for systematic error by multiplying them by a

correction factor as follows:

Correction factor = 1/(1 + systematic error)

where the systematic error is expressed in fractional terms.

Statistical analyses were performed with the SPSS for Windows

statistical package. The means, standard deviations, 95% confidence

limits and the distributions of the errors were computed. The trends

between error and true weight were expressed in terms of Pearson's

moment correlation coefficients.

9.3 Results

Figure 9.1 shows the fetal growth curves obtained for Chitty and

Altman's data using different fetal weight estimation formulae .

For our population, the mean and standard deviations of the errors for

each formula and the correlation of the error with the observed weight

are displayed in table 9.1. Table 9.2 shows the errors at the extremes

of birth weight. All the formulae tested on our population tend to

overestimate true weight, and this applies also to the extremes of fetal

weight (except for Campbell's and Persson's formulae for

weights>4000g). The errors were recalculated after applying the

correction factor for systematic error and the results are shown in

table 9.3.

9.4 Discussion

For any given population, it is apparent from Figure 9.1 that from

about 36 weeks onwards the type of fetal weight formula can have a

marked effect on the apparent fetal growth kinetics, particularly at

term. Hence it is important to select the weight formula that is most

71

suitable for the operator and the population under study. This could

explain some of the variability in the fetal growth kinetics exhibited by

previously published standards, which also appears greatest after 36

weeks.

In our sample all of the formulae studied tended to overestimate the

true fetal weight. The error associated with the use of Hadlock's

formula for BPD, AC and FL was not correlated with the true weight,

whereas all of the other formulae produced errors that were

significantly correlated. Robson and colleagues (1993) also found a

trend to overestimate true fetal weight. They noted a significant

correlation of the error with birth weight for all the formulae they

tested, but these did not include the Hadlock formula included in our

study. This systematic overestimation may arise from differences in

ethnicity between North American and British populations, or in

techniques and equipment. After applying the correction factor for

overestimation, Hadlock's formula for the BPD, AC and FL showed

the smallest SD of the error, and this was the formula selected for our

study. If the BPD could not be measured because of suboptimal

visualisation, the formula used was Hadlock's for the AC and FL. The

modified weight formulae were then applied retrospectively to this

sample and prospectively to the remaining half of the population.

Our approach to selecting weight formulae and correcting for them

makes the assumption that the errors observed at term are similar to

those obtained in the preterm period. To verify this would require a

substantial number of infants born preterm who had had an ultrasound

examination. Another solution is to use echo-planar NMR for accurate

weight estimation in the preterm period and to compare this with the

ultrasonic weight estimates; but this is not as yet an economic

proposition.

72

Table 9.1 Errors of ultrasonic weight formulae in relation to birth

weight for cases delivering within 14 days of examination (N =129).

Formula

Systematic

Error (%)

SD

of Error

Skew

Correlation

(R) with

birth weight

P-value

of R

Persson * 0.43 10.79 0.11 -0.2145 0.015

Campbell 0.89 11.85 0.06 -0.4226 0.000

Hadlock

(AC, FL)

3.23

11.52

0.2

-0.1246

0.159

Warsof

(BPD,AC)

3.78

11.76

0.08

-0.2299

0.009

Hadlock

(BPD,AC,FL)

5.32

1.21

.17

-0.1230

0.165

Shepard

(BPD,AC)

9.24

12.31

0.09

-0.2442

0.005

* original formula using the AD modified to include the AC as follows:

log EFW = 0.972* log BPD + log (AC/3.1416) +0.367*log FL 2.646

73

Table 9.2 Error of ultrasonic weight formulae at the extremes of birth

weight (all cases).

Formula

Birthweight

> 4000g (N = 37)

Mean Error (SD)

Birthweight

< 2500g (N = 17)

Mean Error (SD)

Persson -1.3 (10.36) 7.2 (12.5)

Campbell -3.2 (10.86) 14.0 (16.3)

Hadlock

(AC, FL)

0.9 (10.8)

6.4 (13.8)

Warsof 2.7 (11.8) 11.2 (12.2)

Hadlock

(BPD,AC,FL)

3.4 (10.4)

8.3 (13.2)

Shepard 7.9 (12.3) 17.4 (12.8)

74

Table 9.3 Error of ultrasonic weight formulae for cases delivering

within 14 days of examination after correction for systematic error

(N =129).

Formula Correction

factor

Mean

Error (%)

SD

of Error

Skew

Persson 0.9957 0.00 10.7 0.11

Campbell 0.9911 -0.01 11.7 0.06

Hadlock

(AC, FL)

0.9687

0.00

11.2

0.2

Warsof 0.9636 0.01 11.3 0.08

Hadlock

(BPD,AC,FL)

0.9495

0.00

10.6

0.17

Shepard 0.9154 0.01 11.3 0.09

75

Figure 9.1. Fetal weight curves derived from data by Chitty & Altman.

This is an illustration of how different weight estimation formulae can affect the

kinetics of the resultant fetal growth curve. The curves were obtained from the data

by Chitty and Altman (1994) on the BPD, AC and FL, transformed into fetal weight

according to the weight estimation formulae by Hadlock, Persson, Shepard, Warsof

and Campbell.

76

10. ULTRASONIC STUDY OF FETAL GROWTH: PATIENTS

AND METHODS.

10.1 Introduction

It is apparent from reviewing the literature that considerable

uncertainty exists on fairly basic aspects of fetal growth. Published

ultrasound-based fetal growth standards vary widely, and little is

known on ultrasound-defined fetal growth patterns in human sub-

populations. The analysis of intrauterine fetal weight gain using

ultrasound requires fairly complex techniques, and these are to some

extent arbitrary. Much of our work attempts to address these issues, as

these are at least as important as establishing the clinical validity of

customised growth charts.

10.2 Study Design

Ethics Committee approval was obtained prior to commencing the

study. We aimed to obtain a population sample suitable for the

development of normal standards and to study fetal growth in relation

to maternal characteristics. Inclusion criteria for the study were:

- Singleton pregnancy.

- Maternal age no greater than 35 years.

- Gestational age at booking no greater than 22 weeks.

- 'Low risk' pregnancy at booking.

Smokers and cases who developed pregnancy complications were not

excluded, as long as neonatal outcome was normal on clinical grounds

(see 10.6).

Suitable cases were recruited in the antenatal clinic following their

booking ultrasound examination. Patients were given information

sheets and informed consent was obtained in writing. Women were

examined at intervals of 2-3 weeks commencing at 24 to 32 weeks, for

a maximum of 4 examinations (excluding the booking examination).

77

This schedule allowed us to obtain ultrasound data close to delivery

and in the post-term period. A total of 352 women were recruited. One

patient moved out of the district and delivered elsewhere; delivery

details could not be obtained. Twenty-one cases (6%) did not attend

any planned visits following recruitment; these were excluded from

the analysis. The remaining 325 cases underwent at least one

ultrasound examination. The distribution of the number of

examinations (in addition to the booking ultrasound) is shown in Fig

10.1; 46% attended 4 examinations, 29% attended 3, and the

remainder one or two examinations. In addition to the booking

ultrasound scan, a total of 1021 ultrasound examinations were

performed.

Patients whose fetal growth curves or symphysis-fundus heights were

a cause for concern were referred to their Consultants, but not

excluded unless the neonatal outcome was abnormal.

10.3 Population characteristics

The maternal and neonatal characteristics are summarised in tables

10.1 and 10.2 respectively. As one would expect from the inclusion

criteria, there is a higher proportion of primiparae and a lower

percentage of smokers than in the general population; there are slightly

higher proportions of Europeans and Indo-Pakistanis.

The mean birth weight is about 100g higher than the population

average, and this is explained by the lower preterm delivery rate (5.8%

Vs 7.2%) and lower percentage of smokers.

10.4 Equipment and Methods

Ultrasound examinations were performed using either a Kontron

Sigma 1AC or a Corometrics Aloka 500 with a curvilinear array.

Calculations of gestational length, estimated fetal weight and printing

78

of growth charts were carried out on IBM-compatible personal

computers, using especially designed computer programs.

Measurements of the biparietal diameter, femur length, and abdominal

circumference were taken using standard techniques (Chudleigh &

Pearce, 1986) by one of two experienced operators (JM and AD). The

fundal height was measured in cm with a flexible tape, from the top of

the fundus to the upper border of the symphysis pubis along the

longitudinal axis of the uterus; care was taken to ensure that the

bladder was emptied. Nearly all of these measurements were taken by

one observer (JM).

Maternal height was measured in cm with stadiometers, weights at

booking were measured in Kg using standard scales .Gestation was

calculated in days from the fetal biparietal diameter according to

Campbell's dating chart, using specially-designed computer software.

10.5 Inter-observer and intra-observer variability.

Possible bias in the ultrasound measurements arising from inter-

observer variability was studied in a subset of 12 cases. Measurements

of the BPD and FL were in agreement within +/- 1 mm and thus were

not examined further. The AC is closely related to EFW, and this was

studied in more detail. Twelve randomly selected cases were measured

by both observers. Differences between the paired readings were

tested using the Wilcoxon Matched-Pairs Signed-Ranks Test. No

significant difference between the observers could be detected (Z = -

0.6276, 2-Tailed P = 0.5303). The intra-observer variability was

estimated by measuring the AC of the same baby twice, for 10 cases.

In the case of observer AD, the differences between the two readings

ranged from -1.1 mm to 1.9 mm, the mean being 0.52 (SEM 0.383)

and SD of 1.21, with a normal distribution.

79

10.6 Measures of adverse neonatal outcome

Normal neonatal outcome was defined by the absence of all of the

following:

(a) Congenital abnormalities.

(b) Admission to the Neonatal Intensive Care Unit.

(c) Umbilical cord pH < 7.20.

(d) Umbilical cord base excess < -8.

(e) Apgar score at 5 minutes < 7.

(f) Preterm delivery (<259 days).

Two hundred and eighty-three neonates satisfied these conditions. We

did not use neonatal anthropometric measurements because of their

questionable value, as will be discussed below.

10.7 Calculation of customised and unadjusted fetal and birth weight

centiles.

These were calculated in batches using computer software compiled in

Turbo-Pascal.

Birth weight and fetal weights were entered in grams; gestation was

reckoned in days according to early ultrasound measurement of the

biparietal diameter. For the calculation of unadjusted z-scores and

centiles, the average ‘proportionality curve’ described in chapter 4 was

fitted through the Nottingham birthweight mean of 3443.5g at 280

days and the standard deviation for each gestation was calculated as

11% of the median weight for that gestation. The methods outlined in

chapter 6 were used to calculate the customised centiles and z-scores.

10.7 Discussion

This was essentially an observational study analysed retrospectively.

Its aim was to investigate some basic aspects of fetal growth in the

80

second half of pregnancy and to validate at least some of the principles

on which the customised antenatal growth chart is based.

There is far too much uncertainty on defining normal growth in order

to attempt to draw a valid protocol suitable for a controlled field trial .

This could be one of the reasons why no such trial has to date shown

that ultrasonic screening for fetal growth anomalies improves perinatal

outcome.

We did not exclude cases affected by pregnancy complications such as

pre-eclampsia as long as the condition of the neonate at birth was

normal. Hence mild cases of IUGR and macrosomia are probably

included in the sample, since we believe that there are no reliable and

sensitive techniques to identify them.

Morphometric indices such as the neonatal ponderal index, mid-arm to

head circumference ratio and skin-fold thickness have not been used in

this study.Although they have been employed extensively in

diagnosing IUGR, there is little evidence that they are superior to birth

weight (Chard et al 1992, 1993). On the contrary, the work of Roemer

and colleagues (1991) on over 5000 neonates showed that birth weight

centiles are more closely correlated with acid-base status at birth than

either the ponderal index of Rohrer or the birthweight to length ratio.

Likewise, Wolfe and colleagues (1990) found that the ponderal index

or the weight/length ratio can explain only 52% of the variance in

estimated neonatal body fat; multiple regression analysis of their

sample of 119 neonates showed that birth weight centile and

weight/length ratio were equally good predictors of skin-fold

thickness.

Another weakness of such indices is that they have been derived

from populations whose gestation has been estimated from menstrual

data (Oakley et al ,1977; Georgieff et al, 1988 ). Furthermore, the

measurement of neonatal length is subject to considerable error; the

inter-observer variability being greater than 1 cm in 40% of cases, and

81

the intra-observer variability greater than 1 cm in 15% of cases

(Rosenberg et al, 1992). This error will be cubed in the calculation of

the ponderal index.

Changes in growth velocity as assessed by serial ultrasound have also

been used as indicators of growth disturbances (Chang et al,1993), but

these are poorly related to outcome and in any case have not been

standardised. Deter and colleagues (1990) proposed a neonatal growth

assessment score derived from multiple neonatal measurements

including weight, crown-heel length, head, chest , abdominal and thigh

circumferences, and related to the ultrasonic Rossavik growth

coefficients for these parameters. However, their sample was small (37

infants), and the method has not found wide acceptance.

A case could be made for excluding smokers, as Ott (1988 ) did, but

this may introduce further bias in term of the socio-economic

composition of the population; about 37% already belonged to Class I

or Class II, and by excluding smokers this proportion would increase

significantly, and also reduce the total sample size.

Although a special effort was made to recruit women from non-

European ethnic groups, the final numbers were fairly close to the

overall population norms. These women were less likely to agree to

participate in the study, often because they had large families or could

not afford the extra time for the required additional clinic visits.

82

Table 10.1 Maternal characteristics. Study Group

N = 325 General Population*

Age (years), mean ( SD) 26.7 ( 4.8) 26.5 (5.3) Height (cm), mean ( SD) 163.6 (6.4) 162.3 (6.4) Weight (Kg), mean ( SD) 66.5 (11.8) 65.7 (12.5) Ethnic group (%): European Indo-Pakistani Afro-Caribbean Other

93.8 4.3 1.2 0.6

92.8 3.8 2.5 0.8

Socio-economic groups (%): Class I Class II Class III (M +N) Class IV Class V Unclassified

22 (6.8) 100 (30.8) 119 (36.6) 64 (19.7) 16 ( 4.9) 4 (1.2)

N/A

Parity: Percent of primiparae

49.5

43.8

Smoking: Percent of smokers at booking

16.6

27.0

* Data from Wilcox M, et al (1993b). Table 10.2. Newborn characteristics (N =325). Birth weight (g) mean (SD) range

3406 (553) 1000 - 4900

Sex: percent of males 50.2 Preterm delivery (%) 5.8 Developmental abnormalities (%) 1.5 Admission to neonatal ICU (%) 3.4 Acidosis at birth 3.4

83

11. AN ULTRASOUND STANDARD FOR FETAL WEIGHT

GAIN

11.1 Introduction

The considerable variation in the published literature on normal

intrauterine fetal weight gain as assessed by ultrasound examinations

has prompted us to derive a standard applicable to our local

population. Only one such standard has been published so far for the

United Kingdom (Gallivan et al, 1993), and this was derived from a

sample of 67 cases. Our collection of ultrasound data is larger than

any of the previous studies, enabling us to exclude cases with

abnormal outcome. The purpose of this study is to derive a standard

for fetal growth based on serial ultrasound observations of a normal

population, and to compare this with other norms.

11.2 Materials and Methods

Subjects

The analysis included women who were smokers and

pregnancies which developed complications at a later stage, but

excluded those pregnancies which had an abnormal neonatal outcome,

as defined in chapter 9. A total of 283 of the 352 pregnancies had a

normal outcome by these criteria. After excluding cases who delivered

elsewhere and those who did not attend for at least two ultrasound

scans (in addition to the booking scan) , 267 pregnancies were suitable

for analysis.

Estimation of fetal weight

Ultrasound equipment consisted of either a Kontron Sigma 1AC or a

Corometrics Aloka 500 with curvilinear array transducers. Ultrasound

fetal weight estimation was based on a modified Hadlock's formula

84

for fetal abdominal circumference, femur length and the biparietal

diameter as described in chapter 8.

Modelling of fetal growth

A minimum of 4 points were used to calculate the fetal growth

curve for each individual pregnancy. This included an 18 week fetal

weight value of 223g (Hadlock et al, 1991) which we used as an

invariant point for all cases; at least two EFWs during the third

trimester; and the birth weight. Fetal weight curves were individually

fitted using the least-squares method and a computer program was

written to allow graphical display.

Three growth models were considered:

(a) Simple 2nd or 3rd degree polynomial of gestation.

(b) Rossavik growth model.

(c) Logarithmic transformation of fetal weight expressed as a 2nd or

3rd degree polynomial of gestation.

Although the Rossavik model was slightly better at predicting

the birth weight than the others, the log polynomial model produced a

visually more accurate interpolation. By either method , in nearly all

cases the R-square value was greater than 0.99, as one would expect

by fitting a relatively small number of points. The log-polynomial

model was then applied to all cases thus:

ln(EFW) = a0 +a1 * GA + a2 * GA2 + a3 * GA3

where GA is the gestational age in days according to the booking

ultrasound.

The average curve was obtained by determining the median

weight for each day of gestation, excluding those cases that had

delivered, i.e. there was no extrapolation beyond the observed data.

The standard deviation and the skewness were also calculated for each

day. Functions describing the median curve and the SD were obtained

85

by weighted multiple regression, the number of undelivered cases for

each day acting as the weight.

The growth velocities for our standard and for older standards

were derived by taking the first derivative of the original growth

functions describing the mean curves.

11.3 Results

The medians, standard deviation and skewness for each week are

displayed in table 11.1. Figure 11.1 shows the average growth curve

and the 10th and 90th centiles.

The function describing this curve is as follows:

FWT = 3411.9469-337.82996*GA+9.44545*GA2-0.000000369939*GA6

where FWT is the weight in grams and GA is the gestational age in

exact weeks (e.g. 30.35 weeks).

The distribution of fetal weights was checked for each week of

gestation; although a trend towards positive skewness was noted, it

was not significantly different from normal as assessed by the

Kolgorov-Smirnov test ( p>0.2). The mean birth weight for those

cases delivering between day 273 and day 287 was 3462 g, which is in

close agreement with the predicted 40-week value of the standard

curve (3496g). The Nottingham mean curve is compared with

previously published standards in figure 11.2. The growth velocity

curve is shown in figure11.3, and compared with velocities of

published ultrasound standards in figure11.4.

The kinetics in terms of fractional growth may be derived from figure

11.5, and they are as follows:

G50 = 31 weeks 2 days;

P28 = 34%; P37 = 83%; P42=110%;

86

11.4 Discussion

In deriving this standard we, like all previous workers, made the tacit

assumption that fetal growth is monotonic. Growth spurts have been

documented in preterm infants treated in neonatal intensive care units

(Gairdner & Pearson, 1971), and it is quite possible that they may be

occurring in the intrauterine environment. To be able to detect these

would require a much greater number of observations, and also

considerably more accurate means of estimating both fetal weight and

gestational age than what is available from current technology.

Ours is the only study to our knowledge that combines birth weights

and ultrasound-estimated fetal weights in the derivation of a fetal

growth standard. This is a valid procedure, provided that any

systematic error in the weight estimation formula is corrected in order

to avoid artefactual accelerations or decelerations in the growth curve.

The main advantage of this method is to significantly increase the

range and accuracy of data points into the term and post-term period.

Many of the previously published standards do not give values beyond

40 weeks' gestation, because it is uncommon to perform ultrasound

examinations close to the day of delivery and also because of the small

number of cases recruited. Since the measurements at the booking

ultrasound examination did not allow fetal weight estimation and are

used for precise dating of the pregnancy, we added an invariant 18-

week fetal weight, based on the study by Hadlock and collegues

(1991). This is justified by the fact that the individual variation in size

at such early gestations is consistently small in absolute terms, with

standard deviations ranging from 18.5g (Ott,1988) to 33.5g (Persson

& Weldner,1986 ). The true variation may be even less if imprecision

due to gestational dating is also considered. Early differences in fetal

size, due to physiological or pathological reasons, will exist but unless

extreme, they are not likely to be detected by current techniques of

ultrasound assessment. The inclusion of a fixed 18 week weight point

87

stabilises the growth curve and also allows for accurate interpolation

of fetal weight at the earlier gestations.

There has been some discussion in the recent literature on what should

be the best method of deriving standards of fetal size. Altman (1994)

believes that the method should reflect the purpose of the chart;

longitudinal data should be used for assessment of growth, whereas

cross-sectional data is most suitable for assessment of size. He argues

that individual curve-fitting applied to longitudinal studies would lead

to unduly narrow variances, by 'smoothing ' fluctuations due to

measurement error or growth processes. As Piwoz and colleagues

(1992) pointed out, the resulting centile reference grid would thus be

narrower, and could have potential for misclassification of cases.

Analysis of the published ultrasound-derived fetal weight curves

suggests that this is not likely to lead to major differences. The two

cross-sectional standards of Ott and Hadlock had coefficients of

variation at term of 6.2% and 12.7% respectively, whereas the

longitudinal standards ranged from 9.3% to 19.4% (table 4.1). The

degree of observed variation in the published standards resulting from

population and methodological differences is likely to exceed the

differences that may result from choice of sampling method.

Another methodological issue is whether abnormal cases should be

included. Altman (1994), in describing a cross-sectional study,

believed they should be. We did not include them because their

possibly anomalous growth patterns may distort our standard which is

based on serial data.

On inspection of the graphs, the morphology of our fetal growth curve

appears similar to that of Hadlock and Gallivan, and also to the

average of previously published ultrasound growth curves, in that

there is only minimal deceleration ('flattening') at term. This is

supported by the indices of fractional growth (P50, G28, G37, G42),

which are very similar to these two standards. Our growth model is

88

identical to that used by Gallivan's study, but differs considerably from

Hadlock's cross-sectional study. The coefficient of variation

(SD/EFW) at 40 weeks is 11.6%, very close to Gallivan's 11.5%, but

slightly lower than Hadlock's 12.7% (see table 4.1) . The median fetal

weight values tend to be lower than other standards, and this may be

due to our use of a correction factor to allow for ultrasound

overestimation of fetal weight. It differs considerably from birth

weight standards based on menstrual dates, where the apparent

deceleration at term is more marked. In our local birth weight standard

based on ultrasound-dated pregnancies (Wilcox et al, 1993a), there is

a reversal in the direction of skewness from positive at term to

negative in the preterm period. In contrast, our ultrasound derived

standard shows positive skewness at all gestations except 42 weeks;

the negative value here is probably spurious, since the sample consists

of only 7 cases. The skewness is minimal at around 40 weeks, possibly

because of the stabilizing effect of the birth weight data; some of the

skewness may be due to ultrasound error. These differences between

the ultrasound-derived and the cross-sectional birthweight standards

lend further weight to the theory that a substantial number of preterm

deliveries are associated with intrauterine growth retardation.

89

Table 11.1 Nottingham ultrasound growth standard.

Week *

Sample

size

Median

(grams)

SD 10th

centile

90th

centile

Skewness

24 267 674 109 534 813 0.656

25 267 779 109 639 919 0.645

26 267 899 115 752 1046 0.614

27 267 1033 124 874 1192 0.575

28 267 1180 138 1003 1356 0.539

29 267 1338 155 1140 1537 0.520

30 267 1508 176 1283 1733 0.525

31 267 1688 199 1434 1942 0.547

32 267 1876 224 1590 2163 0.572

33 267 2072 250 1752 2392 0.590

34 267 2273 277 1918 2628 0.592

35 267 2479 304 2089 2868 0.574

36 265 2686 330 2263 3109 0.525

37 262 2894 355 2440 3348 0.444

38 245 3100 376 2618 3582 0.355

39 208 3301 394 2797 3806 0.253

40 135 3496 407 2975 4017 0.159

41 54 3681 414 3152 4211 0.308

42 7 3854 413 3326 4382 -0.692

* Gestation in exact weeks dated by ultrasound.

90

Figure 11.1 Nottingham ultrasound-derived fetal growth standard.

The median, 10th and 90th centile curves for fetal weight are shown from 24 weeks,

derived from 267 women who underwent serial ultrasound examination. The original

individual curves have been forced through a fixed 18-week point and the birth

weight. Gestational age on the x-axis is in exact weeks, calculated on the basis of the

BPD measurement at booking.

91

Figure 11.2 Nottingham fetal growth standard compared with others.

The Nottingham standard is shown as a thick line. Note the similarity with the four

middle standards (Hadlock, Persson, Gallivan and Larsen). Our values tend to be

systematically lower than these, possibly because of the use of a corrected weight

estimation formula.

92

Figure 11.3 Nottingham fetal growth velocity.

The growth velocity in grams per week was obtained by plotting the first derivative of the median for the growth standard. Peak velocity is reached at 36 weeks (210g/week), followed by a rapid decline thereafter

93

Figure 11.4 Nottingham fetal growth velocity compared with others.

The Nottingham growth velocity in grams per week is compared with the

previously published standards of Hadlock et al, Persson & Weldner, Ott,

Larsen et al, Jeanty et al (using Shepard’s formula), Deter et al and Gallivan et

al

94

Figure 11.5 Nottingham proportional fetal growth curve.

The Nottingham median fetal growth curve has been transformed into a

‘proportional’ function, whereby for each week of gestational age the weight is

given as a percentage of the predicted 280 day value. This allows comparison

of growth dynamics independent of the absolute

weight.

95

12. SYMPHYSIS-FUNDUS HEIGHT IN RELATION TO

GESTATION AND FETAL WEIGHT.

12.1 Introduction

Clinical estimation of fetal size, and sometimes gestation, is often

performed by measuring the symphysis-fundus height. This may be

plotted against gestation on a reference chart or, more commonly,

McDonald's rule of equating one centimetre of fundal height for each

week of gestation is used. An alternative clinical method is to estimate

fetal weight by simple palpation.

In the early version of the customised growth chart, a fundal height

axis was included on the right side.This was based on the standard

published by Pearce & Campbell (1987), to allow the evaluation of

fetal size by this parameter.

The aim of this study was to evaluate the relationships between SFH,

gestation and EFW, and also to derive a new scale for the fundal

height axis of the growth chart based on local data. We also examined

the relationships between fundal height growth velocity and

birthweight centiles.

12.2 Patients and methods

For the purpose of obtaining a fetal weight estimation formula based

on SFH, two populations were studied: the derivation set and the

validation set.

The derivation set comprised 284 prospectively recruited low-risk

singleton pregnancies , examined on 3 to 5 occasions prior to

delivery.The exclusion criteria for abnormal neonatal outcome

described in Chapter 9 were applied. The final study group included

267 singleton pregnancies. In order to study the correlations between

SFH and fetal size and SFH with gestation, only one set of

measurements was selected randomly from each patient.Ultrasound

96

and fundal height measurements were performed as described in

Chapter 9.

The validation set consisted of a separate population of 130 unselected

patients who were examined just prior to elective caesarian section or

induction of labour. This was done in order to eliminate error arising

from the lag time between measurement and delivery. The fundal

height, engagement of the presenting part, booking height and weight,

and parity were recorded. Birth weight was entered in grams. Weight

estimation errors were expressed as percentages of the true weight as

follows:

Percentage Error =

100*(Predicted weight - observed weight)/(observed birth weight)

To examine the relationship between mean SFH velocity and

birthweight-for-gestation z-scores, the whole study group was

examined, including cases with abnormal outcome.

Customised and uncustomised birth weight centiles and z-scores were

calculated using the software described in chapter 9. The mean SFH

growth velocity for cases with at least three SFH measurements was

calculated by computer software, fitting a straight line using the least

squares method.

Relationships between pairs were expressed in terms of Pearson's

moment correlation coefficients. Interrelated variables were examined

by stepwise multiple regression analysis, with P<0.05 as the inclusion

criterion.

Statistical analyses were performed using SPSS for Windows (SPSS

Inc.,Chicago]. Graphs were plotted using either the SPSS graphics

facility or Cricket Graph for Windows (Computer Associates, San

Diego, California).

97

12.3 Results

Figures 12.1 and 12.2 are scatterplots of SFH versus EFW and SFH

versus gestation respectively. The correlation between SFH and EFW

is stronger than that between gestation and SFH ( R-square values of

0.74 versus 0.69 ).We found that there was no significant

improvement in weight prediction by allowing for engagement or

including girth (results not shown). A very slight improvement could

be attained by allowing for maternal body mass index .

The regression line for EFW in terms of SFH is :

EFW (grams) = 225.99 * SFH - 5012.29

When this formula was applied to the validation set, the mean error

was found to be -3.7%, with a standard deviation of 18.1%. The

distribution of the errors was not significantly different from normal.

In the 244 cases with more than 2 SFH measurements, there was a

statistically significant positive correlation between SFH velocity and

birthweight z-scores. For customised birthweight z-scores, the Pearson

correlation coefficient was 0.3201 (P<0.0001), which was better than

that between SFH velocity and unadjusted birthweight centiles (R =

0.2921, P<0.0001).

12.4 Discussion

In his review of the study by Lindhard and colleagues (1990)

published in the Cochrane Database, Neilson (1993) cautiously

concluded that ‘it would seem unwise to abandon the use of SFH

measurements unless a much larger trial likewise suggests that it is

unhelpful’. This is a fair representation of the uncertainty in the

literature on the value of this obstetric parameter.

98

Some of the variability in the results obtained by previous workers is

likely to be due to the different approaches in deriving local reference

charts. Unlike most of the previous standards for SFH that show some

deceleration of the curve at term, our study suggested a linear

relationship between gestation and SFH throughout pregnancy. This is

most likely due to the increased precision in gestational age estimation

resulting from the use of early ultrasound measurements, without

using menstrual dates.

If we assume that SFH gradients reflect fetal growth velocity, then the

finding of a better correlation of SFH velocity with customised birth

weight z-scores than with unadjusted z-scores supports the view that

adjusting for pregnancy characteristics gives a better indication of

fetal growth dynamics than otherwise. However, the low values of the

correlation coefficients for these observations implies that mean SFH

velocity on its own is likely to be of limited clinical utility in the

detection of the SGA infant.

Before the advent of obstetric ultrasound, SFH measurement or

clinical palpation were in common use as a way of estimating fetal

size. There is considerable evidence to show that, for fetal weight

estimation, SFH measurement is at least as good as clinical palpation (

Secher,1990; Pschera et al, 1984; Lindhard et al, 1990), and the latter

is accurate to within 450g of the birth weight in 80% of cases

(Loeffler, 1967). However, it is quite possible that clinicians making

fetal weight estimates on the basis of ‘clinical palpation’ may

subconsciously also be using other information to them available, such

as gestational age and maternal size; in Loeffler’s study the patients

were in labour, and the clinicians had full access to all the clinical

data. If this is confirmed in a well designed study, it would mean that

as an objective measure clinical palpation may be inferior to SFH

measurement.

99

Our finding of a better correlation of fundal height with fetal weight

than with gestation is in agreement with the work of De Muylder and

colleagues (1988), and lends some support to using this technique for

growth screening. Furthermore, by using ultrasound fetal weight

estimates, we were able to show that SFH measurements can be used

to estimate fetal weight in the pre-term period. No data has been

published on the accuracy of simple clinical palpation for fetuses

delivering at these gestations. Since clinicians have ‘calibrated’ their

fetal weight estimation skill by clinical palpation on infants delivering

mostly at term, it may well be that in the pre-term period clinical

palpation is not as accurate. However, our figures suggest that the

accuracy of isolated measurements is , not clinically useful unless the

values are extreme. This is not surprising , since fundal height is a

combined measure of maternal and fetal tissues, and is in any case

more likely to reflect fetal length than weight. It may have greater

power if performed serially and frequently , thus allowing trend

analysis, and by the same observer, to reduce error from inter-observer

variability.

100

101

102

13. FETAL GROWTH KINETICS IN RELATION TO

PREGNANCY CHARACTERISTICS .

13.1 Introduction

Although the relationships betwen pregnancy characteristics and birth

weight have been extensively documented, little is known on the

relationships between these characteristics and ultrasound-estimated

fetal weight in the antenatal period.

One of the assumptions of the customised growth chart is that the

factors influencing birth weight are also operative earlier in the third

trimester. In this study, we attempt to explore this issue using three

techniques: (1) graphic display of mean growth curves derived from

different subgroups, (2) non-parametric assessment of differences

between groups, and (3) multiple regression analysis. We also

examine fetal growth among babies born pre-term.

13.2 Patients and methods

Two hundred and sixty seven cases had clinically normal outcome as

described in chapter 5 and also sufficient data points to generate

individual growth curves. Ultrasound-estimated fetal weights were

calculated from the individual growth curves at 26, 28, 30, 32, 34 and

36 to 40 exact weeks, without extrapolation. Hence the samples

became progressively smaller from 37 weeks.

For the purpose of comparing mean growth curves, the population was

subdivided into the following groups: primiparas and multiparas, male

and female fetuses, European and Indo-Pakistani, smokers and non-

smokers, tall vs medium vs short stature, heavy vs medium vs light

maternal weight (at booking). For height and weight, the population

was divided in three equal groups, corresponding approximately to the

33rd and 67th centiles of these variables. Mean growth curves for each

group were derived using the methods described in chapter 10.

103

Descriptive statistics for the individual growth coefficients were

calculated. Differences in birthweight and EFW's at each gestation

were evaluated using non-parametric tests. For two independent

samples, the Mann-Whitney U test was used, whereas for continuous

variables such as maternal height and weight Spearman's rank

correlation coefficient was employed. Stepwise multiple regression

analysis (selection criteria : prob. in = 0.05; prob. out =0.10) was used

to identify factors that were significantly related to fetal weight and

also to birth weight. Categorical variables were recoded as dummy

variables ( 1 or 0), while continuous variables such as height and

weight were entered without transformation. Gestation was not

entered, since the EFW’s were analysed at fixed gestational points (26

to 40 weeks).

In order to compare the growth patterns of the cases born preterm with

the normal cases born at term a different statistical procedure was

adopted, because of the 17 preterm cases only 11 had sufficient

datapoints to generate individual growth curves. An average growth

curve for these cases was plotted by the method described above.

All the ultrasound-estimated fetal weights (up to 37 weeks) for the

normal cases born at term (the control group) were transformed into z-

scores according to the longitudinal fetal growth standard described in

chapter 10, and pooled together to yield a dataset of 736 points. For

the preterm deliveries, both the birth weights and the EFW’s were

transformed into standard deviates by the same growth standard,

leading to a dataset of 55 points. The differences between the means of

these z-scores could then be analysed using Student’s T-test.

Statistical analyses were carried out using SPSS for Windows (ver

6.0). Graphs were plotted using Cricket-Graph graphic software.

104

13.3 Results

A description of the population characteristics is shown in table 13.1;

these are similar to the overall population figures shown in tables 9.1

and 9.2. The distribution of fetal weights at each week did not differ

significantly from normal, as assessed by the Kolgorov-Smirnov test at

the 0.05 level (table 10.1). The fetal growth curves in different

subgroups are shown in Figures 13.1 to 13.7. Ethnic groups other than

Indo-Pakistani were not plotted because of the small numbers in these

categories.

The basic statistics for the individual growth coefficients of the log

polynomial equations are shown in table 13.2. These are significantly

skewed and with unequal variances; furthermore they are closely

correlated to each other (R>0.99 for all pair-wise combinations). The

results of the non-parametric tests are shown in tables 13.3 and 13.4.

The multiple regression constants, coefficients and R-square values

are shown for each week of gestation in table 13.5; the coefficients are

entered as zero if they are not statistically significant.

The differences between the z-scores of EFW’s of pre-term and term

deliveries are shown in table 13.6. Infants delivering pre-term had fetal

and birthweights that were on the average 0.30 standard deviates (or

10 centiles ) below those of babies delivering at term, a difference that

is statistically significant. This analysis was repeated by excluding

birthweights from the preterm group; the latter’s values were still

significantly lower by the Wald-Wolfowitz Runs Test (z =-1.6537, P=

0.0491).The mean growth curve for the preterm group is displayed

graphically in figure 13.7.

13.4 Discussion

Non-parametric and multiple regression analysis of EFW at different

gestational ages confirms that at least some of the pregnancy

characteristics that are affecting birth weight are also operative in the

105

antenatal period. The differences in growth patterns evident in the

graphs for different subgroups are interesting, but are rather refractory

to statistical analysis. Multivariate tests such as Hotelling T2 in

relation to the individual growth coefficients could be applied, but this

would not be a valid exercise because the coefficients have

significantly unequal variances and also a markedly skewed

distribution (table 13.2). These variables cannot be transformed

without changing the interpretation of the results.

The most marked differences in the growth curves are seen in

comparing groups of maternal size .Maternal weight is highly

correlated with EFW from 26 weeks onwards, whereas the effect of

height does not become significant until 32 weeks. This is supported

by the multiple regression analysis, which identifies maternal weight

and its powers as significant throughout the gestational interval

examined.

The growth kinetics for primiparae and multiparae appear similar up

until 36 weeks; thereafter primiparae show some relative slowing in

fetal growth, whereas in multiparae it continues almost linearly. The

differences do not become statistically significant by the non-

parametric test until 39 weeks, although by multiple regression

analysis parity is an independent significant variable except from 32 to

36 weeks. This suggests that most of the known variation in

birthweight due to parity develops late in the third trimester, and may

be related to differences in the intrauterine environment rather than

fetal genetic factors.

Differences in growth between Europeans and Indo-Pakistanis become

significant by the Mann-Whitney U-test in our study from 34 weeks. It

is of interest that although the kinetics of their growth curves are

dissimilar, these differences are not borne out by the multiple

regression analysis. This may be due to differences in maternal size

being responsible for most of the differences, and also the small

106

sample size. The very few cases belonging to the Afro-Caribbean

group and ‘Others’ are nevertheless identified as significant

independent variables intermittently from 26 weeks. Chang and

colleagues (1992) reported a longitudinal ultrasound study on fetal

growth in a sample of 20 Bangladeshi and 67 European women;

statistically significant differences between the mean estimated fetal

weights of the two groups were noted from 28 weeks onwards, but

their analysis did not allow for the confounding effects of maternal

size and parity. Two articles have been published on ultrasonic fetal

growth parameters in different ethnic groups. Vialet and colleagues

(1988) studied two groups from the same location by ultrasound: 201

African women and 201 European women. These were matched by

socioeconomic status and parity. The BPD, FL and AD growth curves

for the two populations were obtained using the Rossavik model. They

found that while the AD growth curves were similar, African fetuses

tended to have significantly smaller BPD's and longer FL's in the

second half of pregnancy. Their birth weights were on average 200g

lighter than Europeans, with no significant differences in the duration

of pregnancy. Although weight gain curves were not produced, these

observed differences suggest that the weight estimation formulae in

current use may yield biased results in this population, since they were

derived from largely European populations.Simmons and colleagues

(1985) were able to study a group of Bengali patients longitudinally;

they measured the BPD and the abdominal area from 14 weeks

gestation, and plotted their results against Campbell and Newman's

standard for Europeans.The mean BPD measurements were below the

median from about 18 weeks, and the abdominal areas were also lower

from about 30 weeks, in both cases never below the 5th centiles. That

their mean birthweight was 300g lower than the European mean is to

be expected, because of the strong correlation of abdominal area with

fetal weight. Both of these studies suggest that fetal growth does not

107

vary significantly within ethnic sub-groups up to about 20 weeks,

maternal influences becoming effective thereafter. This is in

agreement with the animal work discussed in chapter 1 (Snow, 1989),

showing that maternal effects tend to operate late in pregnancy.

Male fetuses were heavier than females from 26 weeks, but these

differences did not reach statistical significance until 36 weeks in our

study. Sex-related differences in individual ultrasound parameters

(BPD, HC and AC) have been reported from as early as 24 weeks

(Parker et al, 1984) .

Cigarette smoking, as reported in mid-trimester, results in lower fetal

weights in the third trimester which in our sample shows statistical

significance from 36 weeks. This is consistent with the effect of

cigarette smoking on birthweight which appears independent of other

characteristics such as maternal height and booking weight (Wilcox et

al, 1993a) .

The lower R-square values in the multiple regression analysis for the

earlier gestations imply that less of the variability in EFW can be

explained by pregnancy characteristics , about 10% as opposed to 22%

at 40 weeks. It is likely that this is due to ultrasound error and

interpolation error in estimating fetal weight for the required

gestations from the growth curves.

Apart from the study by Persson and colleagues (1978) on growth of

the biparietal diameter, previous reports of increased prevalence of

retarded growth among cases born pre-term were based on comparison

of birthweight datasets with ultrasound derived standards (Ott,1993;

Secher et al,1987; Persson, 1989). We were able to show that the z-

scores of EFWs and birthweights of babies born preterm were

significantly lower than the EFWs measured before 37 weeks by the

same standard in babies delivering at term. Since only 30.9% (17/55)

of the data points from the preterm group were birthweights, it is

unlikely that the inclusion of this data could be a source of bias; the

108

Hadlock ultrasound weight estimation formula had been corrected for

systematic error, and also the error from this formula is not correlated

with fetal weight. Exclusion of preterm birthweights from the analysis

still shows lower values for this group, but the statistical significance

is weakened due to the smaller sample. Since our pooled values are

gestation-independent, and also because of the small numbers, we

cannot determine at what gestation these differences become

significant. Persson and colleagues (1978) were able to detect

significantly smaller biparietal diameters in the babies destined to be

born prematurely from 26 weeks onwards, and since this parameter

tends to be relatively ‘spared’ in IUGR, it is possible that fetal weight

differences may exist even before this gestation. It also suggests that

the growth retardation pattern in this group of babies is of the

symmetric type.

Our data show that factors which are known in the first half of

pregnancy - such as maternal height and booking weight, parity and

ethnic group - and which have an effect on birth weight, are also

associated with variation in fetal weight in the third trimester of

pregnancy. These findings suggest that no single standard can

accomodate for the variation of fetal growth, which needs to be

assessed in the context of individual pregnancy characteristics.

109

Table 13.1 Demographic characteristics of

normal population (N = 267).

Ethnic Group: No. (%)

European 251 (94)

Indo-Pakistani 13 (4.9)

Afro-Caribbean 2 (0.7)

Other 1 (0.4)

Smokers: 40 (15.0)

Fetal sex:

Males 135 (50.6)

Parity:

Primiparas 127 (47.6)

110

Figure 13.1 Maternal weight at booking and fetal growth.

Fetal growth is plotted for two groups: those whose mothers had booking weights above the 67th percentile and those below the 33rd percentile. Fetuses of the heavier mothers display accelerated growth from the 26th week of gestational age.

111

Figure 13.2 Maternal height and fetal growth.

Fetal growth is plotted for two groups: those whose mothers had heightsabove the

67th percentile and those below the 33rd percentile. Fetuses of the taller mothers

display accelerated growth from the 32nd week of

gestation.

112

Figure 13.3. Sex and fetal growth.

Fetal growth is plotted for male and female fetuses. Both sexes have a similar growth

pattern, but males are significantly heavier from the 36th week of gestational

age.

113

Figure 13.4 Parity and fetal growth.

Fetal growth is plotted for primiparae and multiparae. Growth follows a similar

pattern until 36 weeks, therafter the primiparae show a slightly decelerative course.

Statistically significant differences are noted from 39

weeks.

114

Figure 13.5 Ethnicity and fetal growth. European vs Indo-Pakistani.

Fetal growth is plotted for Europeans and Indo Pakistani. Growth is slower in the

Indo-Pakistani from about 30 weeks. Statistically significant differences are noted

from 34 weeks, but these could not be shown to be independent of maternal size.

115

Figure 13.6 Effect of smoking on fetal growth.

Fetal growth is plotted for smokers and non-smokers. Fetuses of smoking mothers

are lighter throughout the gestational interval studied. Statistically significant

differences are noted from 36 weeks.

116

Figure 13.7 Preterm delivery and fetal growth.

Fetal growth is plotted for fetuses delivering preterm (<259 days) and those

delivering at term. Fetuses delivering preterm are significantly lighter than

those proceeding to deliver at term.

117

14. CUSTOMISED GROWTH CHARTS IN RELATION TO

NEONATAL OUTCOME.

14.1 Introduction

In order to assess two different methods for the detection of a

condition a third independent method, or 'gold standard', would be

required for an unbiased comparison. Regrettably, in the case of IUGR

there is no generally accepted neonatal test that is accurate and

reproducible. To overcome this difficulty, this study was limited to

comparing the performance of adjusted and unadjusted growth charts

in a population with clinically normal neonatal outcome. This would

allow us to determine which method is best at defining normality, by

giving us estimates of the true negative and false positive rates.

14.2 Materials and methods

Only those cases with normal neonatal outcome, as defined by the

criteria listed in chapter 9, were selected. A total of 267 pregnancies

with a number of ultrasound examinations sufficient to plot individual

growth curves were included in this analysis.

In comparing the customised and the uncustomised growth charts, care

was taken that the standard deviations entered for the two methods

had the same coefficients of variation.

Curve-fitting was carried out according to the method described in

chapter 7. A computer program was developed in order to check

whether the growth curve for each case was wholly within the 10th

and 90th centile boundaries or crossed either from 27 weeks until

delivery. Both customised and uncustomised reference grids were

used. The uncustomised boundaries were defined by using the

'average' proportionality growth curve described in chapter 6, forced

through the Nottingham population mean for 40 exact weeks of

118

3447g. Thus each case could be classified by both customised and

uncustomised criteria in one of four groups as follows:

1. Crosses the 10th centile.

2. Within the 10th and 90th centiles.

3. Crosses the 90th centile.

4. Crosses the 10th and 90th centiles.

Differences between the two methods could then be tested using non-

parametric tests for related samples. Group 4 cases were excluded

from analysis, since these are more likely to contain large errors in

ultrasound fetal weight estimation .

We also investigated the relationship between the standard deviation

entered in the customised growth chart program and the percentage of

cases that would cross the 10th centile boundary. The coefficient of

variation (SD/median) in the computer program described above was

increased in steps of 1 per cent, and the resulting proportion of cases

crossing the 10th centile was plotted.

14.3 Results

Table 14.1 shows the percentage of cases within each group according

to the type of reference boundary. The differences due the

classification method are statistically significant according to the

Wilcoxon Matched-Pairs Signed-Ranks test (table 14.2);

customisation of the reference range results in significantly fewer

normal cases crossing below the 10th centile, but more cases cross the

90th centile. If the population is re-grouped in two categories, as either

crossing or not crossing the 10th centile, the differences due to the

classification method remain highly significant (table 14.3), with

fewer of the normal cases crossing below the adjusted 10th centile

boundary. Fewer cases cross both the 90th and the 10th centile

119

boundaries using the customised compared with the uncustomised

chart (6 Vs 9), but this is not significant. A more detailed breakdown

of the categorical shifts in terms of cases crossing the 10th centile is

given in table 14.4. Many more of the cases labelled as SGA by the

unadjusted method are reclassified as not SGA by the customised

method than the reverse.

In order to study the systematic trends of the two methods, the EFW's

were calculated for all cases from their growth curves at 28 weeks, and

then transformed into customised and uncustomised centiles. The

mean customised centile was 33, whereas the mean uncustomised

centile was 43 (P<0.00001, Wilcoxon Matched-Pairs Signed-Ranks

Test).

Figure 14.1 shows the relationship between the coefficient of variation

of the standard deviation entered in the customised chart program and

the percentage of cases crossing the 10th centile reference line; it is

likely that most of these would be false positives for IUGR.

14.4 Discussion

Statistical analysis of these results suggest that when serial ultrasound

examinations are performed, fewer of the cases with normal outcome

will be labelled as SGA using customised growth charts than using a

fixed reference standard. McNemar's Test is the best method to

analyse this data, because it is a non-parametric test that can be used to

test whether dichotomous variables generated by one method differ

significantly from dichotomous variables generated by another method

applied to the same sample.

A 2 x 2 table is constructed, and the significance level is determined

by either the Chi-square test or by the binomial distribution if fewer

than 25 cases are re-categorised by the second variable.

Our selection criteria for this study - being independent of birthweight

and morphometry - will not exclude the milder, asymptomatic cases of

120

IUGR. Conversely, not all of the neonates with abnormal outcome

have been affected by growth disturbances. These considerations will

prevent us from making an objective assessment of true negative and

false positive rates for IUGR, but should not affect our conclusions

when we are comparing the two methods applied to the same

essentially normal population.

The rather high percentage of cases crossing the 10th and 90th centile

boundaries is likely to be due to fluctuations in the growth trajectory

resulting from ultrasound error, and also the relatively small number

of ultrasound examinations carried out per patient.

As a result the, the 'SGA' rates of 33.2% by uncustomised and 25.5%

by customised growth charts are too high by both methods. This

suggests that the standard deviation generated by the program is too

narrow in view of the ultrasound error. As figure 14.1 shows, the

coefficient of variation should be at least 16% in order to reduce the

percentage of cases labelled as SGA in this normal population to well

under 10%. This approaches the figure of 19.4% in Jeanty's reference

standard (1984). If this growth chart were to be used on its own as is,

the action thresholds would need to be reduced to a degree

commensurate with the local ultrasound error rates in order to avoid

excessive referrals and unnecessary parental anxiety.

Allowing for maternal characteristics in defining the 10th centile cut

off is likely to result in a significant reduction in the false positive rate

for IUGR. Of the cases classified as SGA by the uncustomised

method, 27.5% (25/91) are reclassified as normal by the customised

chart. Conversely, some 2.3% (4/174) of the cases classified as normal

by the unadjusted chart are reclassified as abnormal by the customised

chart. Although a higher proportion of cases may be crossing the 90th

centile using the customised method, this boundary is less related to

neonatal morbidity than the 10th centile (Patterson,1986 ), and hence

less clinically important. Work in progress in our unit on a sample of

121

more than 1000 neonates shows a progressive reduction in neonatal

morbidity with increasing birthweight rank (Dr T. Muls, personal

communication), rather than the U-shaped distribution described by

Patterson between 38 and 41 weeks' gestation .

Our finding of fewer cases crossing the 10th centile boundary is

against an overall trend for customised centiles to be lower than

uncustomised centiles (chapter 14), which is evident for both birth

weights (chapter 14) and EFW. This suggests that the customisation

method is operating more selectively than uncustomised charts.

Further support is the fact that fewer cases cross both centile

boundaries using the customised charts than with the uncustomised

charts.

The design of this study does not allow us to make any statements

about the positive predictive value of customised charts, since as

discussed above we do not have clear criteria to define IUGR. This is

a problem shared with other tests used in perinatal medicine, such as

the biophysical profile, in that they have high negative predictive

values but limited positive predictive values. It is likely that to some

degree this may be due to our limited diagnostic arsenal in identifying

and classifying perinatal pathology.

122

Table 14.1 Percentage of fetal growth curves crossing 10th and 90th

uncustomised and customised centiles for 274 cases with neonatal

normal outcome.

Group Uncustomised Customised

Category No. Number

of cases

% Number

of cases

%

Cross the 10th centile 1 91 33.2 70 25.5

Within 10th and 90th

centiles

2 133 48.5 136 49.6

Cross the 90th centile 3 41 15.0 62 22.6

Cross both 10th and 90th

centiles

4 9 3.3 6 2.2

Total 274 100.

0

274 100.0

123

Table 14.2 Customised versus uncustomised centile boundaries.

Differences in group ranks according to the classification

method.(Group 4 excluded).

Rank 1: crosses 10th centile.

Rank 2: wholly within 10th and 90th centile boundaries.

Rank 3: crosses 90th centile .

Wilcoxon Matched-Pairs Signed-Ranks Test:

Mean rank Cases Direction

24.03 38 - Ranks (UNCUST < CUST)

21.00 8 + Ranks (UNCUST > CUST)

218 Ties (UNCUST = CUST)

Total 264 Z = -4.0697

2-Tailed P <0 .00005

124

Table 14.3 Customised versus uncustomised centile boundaries.

Population reclassified as either Group (1): crosses the 10th centile;

or Group (2): does not cross the 10th centile

( valid N= 265). McNemar Test.

Cust. Gp 2 Cust. Gp 1 Totals

Uncustomised Gp1 25 66 91

Uncustomised Gp2 170 4 174

Totals 195 70

Cases = 265; Chi-square = 13.8; P = 0.0002;

Table 14.4 Shifts in categories according to classification method.

(Group 4 excluded.)

From: To: Frequency Percent

SGA by

customised

not SGA by

uncustomised

4 1.5

SGA by

uncustomised

not SGA by

customised

25 9.4

unchanged unchanged 236 89.1

Total: 265 100.0

125

Figure 14.1 Relationship between coefficient of variation and false positive rate

for SGA.

The graph shows the percentage of individual growth curves crossing the 10th

percentile as a function of the coefficient of variation (SD/EFW), as the latter is

artificially widened or narrowed. The standard deviation needs to be approximately

15.5% of the median weight in order to allow 10% of the population to cross the

10th percentile cut-off.

126

15. THE PREDICTION OF BIRTH WEIGHT

15.1 Introduction

As demonstrated in the preceding chapters, the combination of

ultrasound weight estimation error and error in gestational age can

result in substantial fluctuations in the apparent fetal growth curves, to

the extent that some 25% of cases will cross the 10th centile reference

line using customised growth charts. Birth weight, although it is not an

accurate indicator of individual growth kinetics, remains one of the

most reliable measurements in this study. Both the customised growth

chart and the IBR programs base their calculations on a predicted birth

weight at a given gestation. It is important therefore to compare the

predictive ability of the adjusted versus the unadjusted standards.We

also analysed the birthweights in our normal sample using three

standardisation methods: the customised growth chart, the unadjusted

standard and the IBR .

15.2 Patients and Methods

We analysed both the population with normal outcome (N = 282) and

also those with abnormal outcome ( N = 42), to see the extent the

weight prediction error would be increased by including pathological

cases. The unadjusted growth standard was that described in chapter

13, whereas the customised program was the same as described in

chapter 6. Software for the calculation of the IBR and IBR centiles

was compiled with the assistance of Mr Mark Wilcox.

The predictive ability of the customised and uncustomised standards

was assessed by calculating the signed and the absolute prediction

errors for each case, both in grams and as a percentage of the true

weight.

The percentage error was calculated thus:

127

Percent Error = 100 * (Predicted - Observed)/Observed

This was done by modifying the computer programs described in

chapter 13 ; the predicted weight by the customised program was

calculated using the multiple regression model, while by the

unadjusted standard the corresponding value was the median for

gestation.

The cumulative frequency distribution of the errors by both methods

was then plotted.

The significance level of the differences between the two methods was

calculated by applying Wilcoxon matched-pairs signed-ranks test.

The frequency distribution of the number of cases in each decile

category according to the different birthweight standards were plotted

as histograms, for the overall population and also for non-smokers,

since smoking is probably the most common cause of mild IUGR.

In order to examine time trends in relation to customised and IBR

centiles, the centile difference between the two was calculated for each

case and plotted as a function of gestation. The strength of this

relationship was quantified by calculating Spearman's rank correlation

coefficient.

The relationships between customised, uncustomised and IBR centiles

were also studied using Spearman's rank correlation coefficients, and

displayed graphically.

The percentile values obtained by the three methods were categorised

in ten groups, 1 to 10, according to the corresponding decile values.

Differences in categorisation between two standards were tested using

the Wilcoxon matched pairs signed-ranks test, for the three possible

combinations: customised vs uncustomised, customised vs IBR and

uncustomised vs IBR .

128

15.3 Results

The descriptive statistics (including the Kolgomorov-Smirnov test for

normality) for the term birthweights in the population with normal

outcome are shown in table 15.1. The distribution did not differ

significantly from normal, with only a very slight degree of positive

skewness.

The customised growth chart program was significantly better at

predicting birthweights than the unadjusted chart, with a mean

absolute error of 303.5 g versus 342.6 g respectively (z = -4.11,P <

0.00001). The cumulative frequency distribution of the absolute error

for the two methods is shown in figure 15.1. The error was less than

250g in 50% of the population using multiple regression, whereas

using the unadjusted, gestation-specific standard this figure was

reduced to 46% The Spearman correlation coefficient between the

predicted weight by multiple regression and the birth weight was

0.9642.

The standard deviation of the signed prediction errors for the normal

population was 5.35% of the true weights for the customised program

and 5.63% using the unadjusted method. The corresponding figures in

the neonates with adverse outcomes were 7.65% and 8.02% (table

15.2 ).

The frequency distributions of deciles according to the three different

methods tested are shown as histograms in figures 15.2 to 15.4.

These are significantly different from each other, as detailed in table

15.2. Frequency distributions were also drawn for the non-smokers

and these are shown in figures 15.5 to 15.7. For the unadjusted

standard, the exclusion of smokers results in a reduction of cases

below the 10th centile by 1.7% while the corresponding figures for the

IBR and customised chart are 1.1% and 2.8%. Spearman's correlation

coefficient for customised and IBR centiles was 0.9939; the scatterplot

for these values is shown in figure 15.8. A highly significant negative

129

correlation was noted between gestation and the difference between

customised and IBR centiles (R = -0.607, P< 0.0001 ).This is shown in

figure 15.9.

15.4 Discussion

The multiple regression model in use by the adjustable standards

performs significantly better than the unadjusted standard in predicting

birth weights. What is more remarkable is that the overall predictive

performance of the models using gestation alone or gestation in

combination with maternal characteristics is superior to that of the

ultrasound weight-estimation formula used in our study (SD of

ultrasound error: 10.2%, table 9.3). Even in the abnormal population,

where one would expect an increased prevalence of growth disorders,

the SD of the error using multiple regression (7.65%) matches the best

reported figures in the ultrasound literature (Hadlock et al, 1985). In

the multiple regression analysis of the East Midlands Obstetric

Database, the standard error of the regression model, which was

centred on 40 weeks, was 389 grams , which is about 11% of the

overall mean birthweight at 40 weeks (3443g). The reason for the

improved performance of the model in our population is probably due

to the presence of frequent inaccurate values in the obstetric database,

as opposed to our carefully checked entries in our study group.

That multiple regression using pregnancy characteristics only may be

as good as ultrasound was also suggested by Rogers and colleagues

(1993) in Hong Kong. They developed a multiple regression model for

the prediction of birthweight (inclusive of the same variables as our

model) from half of a Chinese database of 23750 singleton deliveries,

and analysed its performance by testing it against the other half. The

standard deviation of their absolute errors was 130g/Kg, i.e.13% of

actual birthweight. The corresponding value in our sample was at

worst 4.32% (table 15.2).

130

Likewise the correlation coefficient between predicted and

birthweights in their sample was 0.53, as opposed to ours of 0.96.

Their poorer performance is almost certainly due to the fact that their

population was not routinely dated by ultrasound, and not selected on

the basis of pregnancy outcome. These findings suggest that accurate

knowledge of only two factors is sufficient in order to make

reasonable fetal weight estimates: a population-specific average

growth curve and gestational age. It also emphasises the effect of

gestational age on fetal weight.

The use of the two adjusted standards results in a much smoother

distribution of deciles than the unadjusted standard. This means that

the random fluctuations in the centile values of the unadjusted

standard due to the small sample size tend to even out when processed

by the customised standard together with the variation in pregnancy

characteristics. It may well be that this feature of the adjustable

standards makes them more suitable for analysing small data sets.

There is a relatively high number of cases below the 20th centile

using the unadjusted and the customised standards (26.5% and 22.6%

respectively, versus the expected 20%) , while the corresponding

figure for the IBR is 19.7%.

The customised charts results in values that are usually lower than the

IBR centiles; this is probably because the customised growth chart is

predicting birthweights for a non-smoking population, whereas in the

IBR program the smoking factor is ignored.

This difference in the way the two programs handle the influence of

smoking is also reflected in the changes in the percentage of cases

below the 10th centile when the smokers are excluded from the term

population. This change is greatest for the customised growth chart,

which resulted in 2.8% fewer cases being labelled as ‘SGA’ when the

smokers were excluded.

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The highly significant negative correlation between gestational age

and the difference between customised and IBR centiles is likely to be

due to the different growth functions; the customised growth chart

uses an average function which is almost a linear relationship between

fetal weight and gestation, whereas the IBR uses the birthweight curve

derived from the East Midlands Obstetric Database, which shows

some degree of 'flattening' at term. Hence with increasing gestation,

the difference in birthweight expectation between the customised and

the IBR programs widens, resulting in an increasing difference

between the centiles calculated by the two methods. This implies that,

of the infants born post-term, more would be labelled as ‘SGA’ by the

customised standard than using the IBR program, suggesting greater

sensitivity of the customised standard at this critical gestational age.

The very high correlation between the IBR and customised centiles is

indirect evidence in favour of the clinical efficacy of the customised

chart program, since the IBR program has been shown by Sanderson

and colleagues (1993) to be better able at detecting IUGR than the

unadjusted birthweight standard.

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Table 15.1 Descriptive statistics for term birth weights for infants with normal outcome. Mean (SD) 3474.8 (490.8) g S.E. of mean 29.2 Skewness (SE) 0.086 (0.145) Kolmogorov-Smirnov test for normal distribution

z = 0.6248 P = 0.8298

Range 2890 - 4900 g Valid No. 283 Table 15.2 Analysis of birthweight prediction errors in the populations with normal and abnormal neonatal outcomes.

Sample Method Systematic Error (%)

Standard Deviation (%)

Mean Absolute Error (%)

Standard Deviation (%)

Normal (N=283)

Unadjusted (Gestation only)

0.67 5.63 4.93 2.79

Multiple Regression Model

0.18 5.39 4.57 2.81

Abnormal (N= 42 )

Unadjusted (Gestation only)

0.65 8.02 6.71 4.32

Multiple Regression Model

-0.94 7.65 6.39 4.19

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Table 15.3 Differences in classification by standardisation method. Wilcoxon matched-pairs signed-ranks test for the population of infants born at term. Pair comparison: A with B

A<B A>B A = B (ties)

Number

Mean rank

P-value

Customised with IBR

Customised < IBR Customised > IBR Customised = IBR

161 0 121

81.0 0.0

< 0.00001

Uncustomised with customised

Uncust < cust Uncust > cust Uncust = cust

113 54 115

91.32 68.68

< 0.00001

Uncustomised with IBR

Uncust < IBR Uncust > IBR Uncust = IBR

171 18 94

99.47 52.50

< 0.00001

134

Figure 15.1 Birth weight prediction errors: customised vs uncustomised charts.

The graph shows the cumulative percentage of absolute birth weight prediction

errors using either the pregnancy characteristics-adjusted (‘customised’) or

unadjusted (‘uncustomised’) charts when applied to the population with normal

neonatal outcome. Customised charts give significantly better predictions than the

unadjusted charts.

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136

137

138

139

140

141

142

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16. COMMENTS AND CONCLUSIONS

Adjusting for pregnancy characteristics.

The concept that growth standards should be adjustable according to

pregnancy characteristics has been rather controversial. It was

postulated by Thomson and colleagues (1968), and was based on the

known relationships between these characteristics and birth weight.

Their birth weight standard was parity and sex-specific; the adjustment

coefficients for maternal height and weight were fixed, because not

enough data was available to analyse gestational-age dependent

changes. They did not provide any clinical evidence, however, that

adjusting for maternal characteristics leads to superior performance

than unadjusted charts, and their arguments were based on rather

intuitive grounds. These issues were debated in a discussion chaired

by Professor Whittle (1989), and attended by Nicolaides, Alberman,

Wigglesworth, Steer, Campbell and others. It was pointed out that

although differences in mean birthweights between subgroups may

appear small, shifts of such magnitude will affect the tails of the

distributions significantly and these can actually be associated with

important changes in perinatal mortality.We also have found that

small shifts in the median values will lead to major changes in the

number of cases that are reclassified as SGA (Gardosi et al, 1994). It

was agreed at this discussion that it is legitimate to correct for sex and

plurality, because in these instances the mortality of the smaller group

is lower; but no agreement could be reached on parity and maternal

weight. It was felt that because maternal malnutrition was a common

cause of IUGR in the developing world it is not legitimate to adjust

for maternal weight. Steer believed the effect of parity to be mediated

through maternal pre-conceptional weight, and therefore should not be

considered as an independent adjustment factor. He referred to work

by Van Der Spuy and collegues (1983), purporting to show that

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women who were underweight at the time of conception (as defined

by the body-mass index) had double the risk of preterm delivery and a

three-fold increase in the incidence of SGA.This was even greater if

ovulation was induced. While this may be true for malnourished

women, it disregards the relationship between maternal weight and

birthweight for women within the normal range of body mass index.

Another limitation of this study is that maternal weights were recorded

at booking (<16 weeks), rather than pre-pregnancy. This introduces

the confounding factor of weight gain, which may be considerable by

16 weeks, and is significantly correlated with birth weight. In our

multiple regression analysis of the East Midlands Obstetric Database

we found parity to be a significant and independent factor influencing

birth weight (Wilcox et al, 1993a). This has also been the experience

of other workers (Thomson et al, 1968; Voigt et al, 1989).

Our data shows that in terms of predicting birth weight, adjusting for

pregnancy characteristics is significantly more accurate than using

gestational age alone.

We were able to show that in our sample the factors influencing birth

weight are also operative in the antenatal period from as early as 26

weeks. There seems a rough inverse relationship between the

importance of the maternal factor and the apparent gestational age of

onset of the factor, with the stronger factors being operative from an

earlier stage than the weaker factors. Parity, maternal weight, and

ethnic group appear to be major independent significant variables,

although less of the variability in EFW can be explained by these

factors , probably because of ultrasound error. This supports the

principle of adjusting the standard for pregnancy characteristics

throughout the third trimester.

The origin of the observed differences in fetal size within maternal

subgroups remain obscure. The studies using animal models suggest

that genetic influences do not become important until the second half

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of pregnancy, and this is supported by the human studies showing

minimal inter-ethnic differences in the ultrasound dating parameters. It

is not possible at the moment to determine whether the differences in

birth weights between two groups are due to a preponderance of

growth stimulatory effects in one or growth retarding effects in the

other. It may be that effects of physiological origin are mediated by

growth stimulatory factors, whereas differences due to pathological

factors are mediated by growth retarding factors.

Design of the customised growth chart.

We found the prediction of birth weight using the multiple regression

model to be unexpectedly good. Given gestation and pregnancy

characteristics, the accuracy of the model was in fact better than the

ultrasound fetal weight estimation formula we used, and matches the

published figures for such formulae. This degree of accuracy is most

likely to be due to the use of early ultrasound in the estimation of

gestational age, and it supports the validity of the ‘proportional’ fetal

growth curve. This method is unlikely to be accurate in cases where

some degree of growth disturbances is suspected, since the multiple

regression model is designed to predict median values. Although it is

effective in predicting weight, we do not know whether the assigned

percentile values are indeed better related to perinatal morbidity and

mortality.

Some of the other principles on which the customised growth chart is

based upon remain open to discussion. The method of deriving the

adjustment coefficients for pregnancy characteristics by multiple

regression analysis was a compromise between selecting a 'supra-

normal', non-smoking population and an unselected population

without making any allowance for smoking. Other methods of

obtaining these coefficients should be explored, as they could possibly

lead to improved accuracy.As maternal weight is one of the most

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important adjustment parameters, the customised growth chart

program has a 'range-checking' mechanism to prevent making

inappropriate adjustments for weight when there is evidence of

malnutrition or gross obesity. This is based on the normal values of

body mass index in mid-pregnancy.

An alternative technique to using multiple regression for the

prediction of term birth weight is a computer neural network. This is a

method used in artificial intelligence whereby observational data

related to a particular outcome is fed repeatedly to a multi-layered

network of inter-related 'neurones', which will then form weighted

connections. The network will then be able to make predictions on

outcome when faced with a new set of data. This has been applied to

the East Midlands Obstetric Database, and interestingly the predictive

power of the network was not superior to the multiple regression

model (Mr Mark Wilcox, personal communication).

The use of a single fractional fetal growth curve derived from an

average of previous studies may be questioned. Prior to our work,

there was no ultrasound data on fetal weight to suggest major

morphological differences between subgroups, and hence there was no

alternative to using a single type of growth curve.

Apart from the evidence favouring EFW over the individual

ultrasound measurements as a screening tool for growth disturbances

(Chang et al,1993; Hedriana & Moore,1994), the main reason we

chose this parameter is that the adjustment coefficients used in the

customised growth chart have been derived from birthweight data.

Another reason is that very little has been published on the

relationship between pregnancy characteristics and individual

ultrasound parameters.

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Fetal Size Versus Growth Velocity

There has been considerable debate in the recent literature over the

relative merits of fetal size and growth velocity in the prediction of

fetal compromise (Gardosi, 1994; Chang, 1993; Danielian, 1993).

While it is indisputable that fetal size at any one time is the result of

average growth velocity since conception, it is also likely that

disturbances in growth velocity immediately before that time may be

of pathological significance. For instance, at a given gestation a fetus

may be of above average size because of a high average velocity, but

growth retardation may have been occurring for the previous few

weeks. Some evidence to support growth velocity as a predictor of

poor neonatal outcome was provided by the work of Chang and

colleagues (1993).

In this study, changes in the standard deviation scores of EFW and AC

between the first and the last ultrasound examination were compared

with the final values of the AC, EFW and Doppler indices. Poor

neonatal outcome was defined in terms of morphometric indices of

IUGR. It was found that serial assessment of EFW and AC was an

overall better predictor of IUGR than the other parameters. One of the

difficulties of this study was that the changes in the standard deviate

scores were not expressed per unit time, and hence mathematically

these are not accurate measures of velocity. Another issue is the

validity of the morphometric indices; if the analysis is restricted to

subscapular skinfold thickness - which is perhaps the most logical

index of IUGR- there appear to be only minor differences in the areas

under the ROC curves for all the parameters measured.

One of the practical problems with using growth velocity is the

additive effect of the ultrasound errors of two or more measurements;

in order to compensate for this error, frequent serial measurements

would have to be taken to observe significant trends. Another problem

is the timing interval. The importance of this factor has been discussed

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in correspondence by Gardosi (1994). The shorter the timing interval,

the earlier the detection of anomalies but greater the relative error in

estimating velocity; on the other hand, lengthening the interval will

make timely intervention difficult. An additional difficulty is the lack

of any practical published standard for growth velocity.

It is probable that both fetal size and growth velocity need to evaluated

in the optimal assessment of fetal well-being. The often-made

statement that 'size does not matter as long as the baby is growing

well' is based on very little empirical evidence, and may well be

misleading in a clinical context.

Defining IUGR in the neonate.

The ideal method to compare the performance of different tests on the

same sample is by analysing receiver-operating characteristic curves

(Zweig & Campbell, 1993) . This requires a clear division between

affected and unaffected populations. In our sample this could not be

made because of the lack of a 'gold standard' in defining either IUGR

or macrosomia. Hence our finding of significantly lower rate of SGA

in a normal population using the adjustable standard does not imply a

better positive predictive value or detection rate for IUGR, since this

could not be tested against a sample of 'truly' growth retarded fetuses.

Nevertheless, indirect evidence that this may be the case comes from

the work of Sanderson and colleagues (1994 ), who found that the

individualised birth weight ratio is more closely related to

morphometric indices of IUGR, including skinfold thickness. We have

found a very high correlation (r = 0.99) between customised centiles

and IBR centiles ; this is because the method of adjusting for maternal

characteristics is the same, differences being due to the growth curve

selected and in determining the standard deviation for a given

gestational age.

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Our choice of the 10th centile as the definition of SGA was arbitrary,

based on conventional clinical practice, and we are certainly not

advocating this as a definition of IUGR.

Chard and colleagues (1993) have argued that fetal size for gestation

at birth, as opposed to maturity, is irrelevant to outcome and have

questioned the existence of fetal growth retardation at term. They

believe that most small term infants are not at risk, and that a

considerable number of babies thought to be small for dates are only

so because of inaccurate gestational age estimation. This may well be

so because of the imprecision in estimating weight -for-gestation rank

resulting from error in gestational age assignment and the use of

inappropriate, unadjusted birthweight standards (eg Lubchenko's

standard applied to a sea-level population ). No epidemiological

studies have yet been published on abnormal neonatal outcome or

neurodevelopmental disability in pregnancies dated by

ultrasonography. The study on neurodevelopmental handicap by

Taylor and Howie (1989) showed that affected infants were

significantly lighter at birth than controls, with lower birthweight

centiles, but only when complications of pregnancy were present. In

their sample only 16% of the population was pre-term, but at the time

these children were born, gestational dating by ultrasound was still in

its infancy, and hence this figure is suspect. Work in progress in our

unit on ultrasound-dated populations has shown a clear, inverse

relationship between birthweight centiles and neonatal morbidity,

which is even more marked for customised birthweight centiles (Dr

Muls, personal communications). Hence we believe that there is a role

for estimating fetal size in antenatal risk assessment.

Defining normal fetal growth.

Our work was essentially a longitudinal, observational study designed

to investigate fetal growth in the third trimester and to explore the

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clinical viability of customised growth charts. For this purpose we

recruited a 'low-risk' population in order to focus on normal fetal

development. The choice of ultrasound weight-estimating formula was

an important aspect of the project. We studied this problem (chapter

9) in a subset of the population that delivered near term. We had to

make the assumption that the findings applicable to this group and the

correction factors for the modified weight-estimation formulas would

also be valid in the pre-term period, as we did not have a sufficiently

large group of infants born below 37 weeks’ gestation. In choosing an

appropriate formula, accuracy is a major consideration, but another

important issue which is often disregarded is the correlation of the

error with the size of the fetus. Such a correlation, if significant , will

lead to data distortion at the extremes of the measured range. We

found that the Hadlock formula for BPD, AC and FL was not only the

most accurate, but also was free of any significant trend in the error.

We decided to develop a longitudinally-derived growth standard , as

opposed to a cross-sectional one, because in clinical practice the

EFW's of high-risk fetuses are usually plotted serially. As pointed out

by Altmann (1994), longitudinal data is most suitable for defining

growth process. The variance would thus be somewhat smaller than

that derived from a cross-sectional standard , depending on the degree

of ultrasound error, but the median curve should remain unchanged.

As a consequence, the 10th centile may be higher than if the standard

was obtained by cross-sectional analysis. In clinical terms, this would

lead to an increased test sensitivity but a reduction in specificity,

which can be easily corrected by shifting the 'action threshold'

downwards, say the 5th centile instead of 10th. Using the standard

error of the multiple regression analysis in order to derive the standard

deviation for the growth charts may be criticised in that it leads to

reference ranges that are too narrow in view of the ultrasound error.

We feel that it is not practical to widen the standard deviation to

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accomodate ultrasound error in EFW because the latter varies

considerably according to formula and the local conditions. The

decision of which action threshold to use thus needs to be dealt with at

a local level in view the performance in estimating fetal weight.

Our finding of a virtually linear relationship between fetal weight and

gestation using serial ultrasound data is at variance with older

standards derived from samples whose gestational age is estimated

from menstrual data. These are characterised by marked deceleration

of growth at term. To a large extent our linear relationship is due to

the improved accuracy in gestational age estimation using early BPD

measurements; another factor is our population selection criteria

which excludes neonates with abnormal outcome and likely

anomalous growth patterns. It is unlikely to be an artefact due to the

fetal weight estimation formula, since the error associated with the

Hadlock formula we used was not correlated with fetal size and also

because of the stabilising effect of including birthweights in the

individual growth curves.

Preterm Delivery and Growth Retardation.

In a substantial proportion of preterm deliveries the cause is unknown,

or undetected.

We were able to confirm previous reports (Ott,1993; Secher,1987;

Persson,1989) of impaired fetal growth in infants born pre-term, using

an ultrasound-derived fetal growth standard applied to both the EFWs

and birth weights of infants born preterm. In clinical practice these

cases are usually missed, either because of the use of cross -sectional,

birthweight-derived reference standards or from the inaccuracy of

ultrasound measurements.

We also found that the distribution of our ultrasonic fetal weight

estimates was positively skewed, but not to a statistically significant

degree. This is in contrast to the significant negative skewness of the

152

preterm birthweight data (Wilcox, 1993) and corroborates the concept

that a significant proportion of the infants born preterm may be growth

retarded.

In view of the poor detection rate of growth retardation using current

methods, the practice of long-term tocolysis in pregnancies with

threatened preterm labour should be viewed with caution. Preterm

delivery may well be an escape mechanism for those babies whose

nutritional needs are not being met by the utero-placental unit, and its

pharmacological suppression could lead to deterioration in fetal

condition.

Recommendations for further research.

It will be difficult to improve the predictive value of any method for

the detection of growth disturbances without first developing an

accurate, quantitative standard for the diagnosis of IUGR in the

newborn. The problems arising from the lack of a gold standard in the

definition of deficient growth were discussed by Keirse (1984), and it

is of interest that little progress has been made since then. Ideally this

standard should give an indication of the energy stores of the neonate,

since these are depleted in conditions of sub-acute or chronic hypoxia.

This is a field of research which is being actively pursued in our

department. An alternative to using indices of IUGR is to use actual

clinical outcomes, such as acid-base status, admission to neonatal

intensive care, etc..This allows the population to be divided two or

more groups, according to neonatal outcome. The problem with this

approach is that the pathological group will be heterogeneous, with

only some of the morbidity being due to growth retardation. Hence the

positive predictive value of the test cannot be clearly defined, unless it

is restricted to clear-cut clinical conditions within a sufficiently large

sample.

153

It may be possible that a single method of adjusting for maternal

characteristics may not work optimally for all conditions, so that for

instance a system that works optimally for IUGR may not be as

effective in screening for macrosomia. Alternative methods of

adjusting for maternal smoking need to be evaluated. In order to

develop optimal customisation methods, large databases of cases with

a variety of abnormal outcomes would be needed.

The current program uses a single fractional growth curve for all

maternal subgroups. Our work suggests that maternal subgroups may

differ in the shape of their fractional curves, as is the case for

nulliparous and multiparous populations. An issue to explore is

whether better performance from the customised growth chart may be

obtained by using more than one type of fractional growth curve. In

other words, the customisation process could perhaps be optimal if it

includes not only the prediction of birth weight at term, but also the

likely shape of the growth curve.

The distribution of our ultrasound fetal weight estimates in the normal

sample showed a non-significant trend towards positive skewness at

all gestations, which agrees with the observed significant positive

skewness in term birthweights extracted from the East Midlands

Obstetric Database. The customised growth chart does not incorporate

this skewness, making the assumption of normality at all gestations. It

would be a relatively simple procedure to reproduce this skewness in

the computer program, and it would be interesting to compare the

efficacy of this version of the program.

The multiple regression coefficients allowing the prediction of birth

weight at term were derived from a database that has been

accumulating over many years. It is possible that, if the population

characteristics change significantly with time, the coefficients may

also change. The significance of this issue needs to be investigated by

extracting the coefficients from the East Midlands Obstetric Database

154

for a series of time intervals. If this is the case, then the performance

of the customised growth chart could improved by using 'up-dated'

coefficients.

Irrespective of whether adjusted or unadjusted charts are used, the

error in fetal size estimation is a major limiting factor to the test

performance. It is a possible explanation for the relative lack of

success of screening programs using ultrasonic fetal growth

parameters. Two approaches are possible in order to tackle this

problem. Firstly, better formulas may be developed that, in addition to

the AC, include other measures of soft tissue, such as limb

circumferences (Balouet et al, 1992). Secondly, more advanced

equipment is likely to lead to better results. Three-dimensional

ultrasound machines, by improving the definition of tissue planes,

should reduce the error in measuring ultrasound parameters, and will

also allow more reproducible measuring of limb circumferences. The

use of echo-planar magnetic resonance imaging (Baker et al, 1994) is a

very promising technique for weight estimation, but will not be in

common use until its prohibitive cost is reduced drastically. This

should be case once low-cost high temperature super-conductors are

available. Another significant source of error is the inter-observer

variability, which for ultrasonic weight estimation by two observers

ranges from -187g to 140g (Chang et al, 1993). Increasing the number

of observers will result in greater variability. The ideal of having only

one observer performing all the serial examinations for the same

patient is probably unattainable in a busy obstetric ultrasound

department, but should be within reach of community midwifery care,

using symphysis-fundus height measurements. A community-based

project is now underway in order to assess the value of serial SFH

measurements performed by the same observer and plotted on the

customised charts in terms of detecting growth disturbances. Cases

that are screen-positive on the basis of abnormal serial or single SFH

155

measurements are referred for ultrasound examination and biophysical

profiles including Doppler when indicated.

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