Association of Procalcitonin With Acute Pyelonephritis and ... Club/Crib Notes for... · Association of Procalcitonin With Acute Pyelonephritis and Renal Scars in Pediatric UTI WHAT’S

Association of Procalcitonin With Acute Pyelonephritis

and Renal Scars in Pediatric UTI

WHAT’S KNOWN ON THIS SUBJECT: Prompt, high-quality

diagnosis of acute pyelonephritis and later identification of

children with scarring are important to prevent future

complications. Examination by dimercaptosuccinic acid scan is

the current clinical gold standard but is not routinely performed.

WHAT THIS STUDY ADDS: Procalcitonin demonstrated a more

robust predictive ability, compared with C-reactive protein or

white blood cell count, to selectively identify both children who

had acute pyelonephritis during the early stage of urinary tract

infections, as well as those with late scarring.

abstractBACKGROUND AND OBJECTIVE: Urinary tract infections (UTIs) are com-

mon childhood bacterial infections that may involve renal parenchymal

infection (acute pyelonephritis [APN]) followed by late scarring.

Prompt, high-quality diagnosis of APN and later identification of

children with scarring are important for preventing future complications.

Examination via dimercaptosuccinic acid scanning is the current clinical

gold standard but is not routinely performed. A more accessible assay

could therefore prove useful. Our goal was to study procalcitonin as

a predictor for both APN and scarring in children with UTI.

METHODS: A systematic review and meta-analysis of individual patient

data were performed; all data were gathered from children with UTIs who

had undergone both procalcitonin measurement and dimercaptosuccinic

acid scanning.

RESULTS: A total of 1011 patients (APN in 60.6%, late scarring in 25.7%)

were included from 18 studies. Procalcitonin as a continuous, class, and

binary variable was associated with APN and scarring (P , .001) and

demonstrated a significantly higher (P , .05) area under the receiver

operating characteristic curve than either C-reactive protein or white

blood cell count for both pathologies. Procalcitonin $0.5 ng/mL yielded

an adjusted odds ratio of 7.9 (95% confidence interval [CI]: 5.8–10.9)

with 71% sensitivity (95% CI: 67–74) and 72% specificity (95% CI: 67–76)

for APN. Procalcitonin $0.5 ng/mL was significantly associated with

late scarring (adjusted odds ratio: 3.4 [95% CI: 2.1–5.7]) with 79%

sensitivity (95% CI: 71–85) and 50% specificity (95% CI: 45–54).

CONCLUSIONS: Procalcitonin was a more robust predictor compared

with C-reactive protein or white blood cell count for selectively

identifying children who had APN during the early stages of UTI, as

well as those with late scarring. Pediatrics 2013;131:870–879

AUTHORS: Sandrine Leroy, MD, PhD,a,b Anna Fernandez-

Lopez, MD,c Roya Nikfar, MD,d Carla Romanello, MD,e

François Bouissou, MD,f Alain Gervaix, MD,g Metin K.

Gurgoze, MD,h Silvia Bressan, MD,i Vladislav Smolkin, MD,j

David Tuerlinckx, MD,k Constantinos J. Stefanidis, MD,l

Georgos Vaos, MD,m Pierre Leblond, MD,n Firat Gungor,

MD,o Dominique Gendrel, MD,p and Martin Chalumeau, MD,

PhDp,q

aCentre for Statistics in Medicine, University of Oxford, Oxford,

United Kingdom; bEpidemiology of Emerging Disease Unit, Institut

Pasteur, Paris, France; cDepartment of Pediatrics, Joan de Deu

Hospital, Barcelona, Spain; dDepartment of Pediatrics, Abuzar

Children Medical Center Hospital, Ahvaz, Iran; eDepartment of

Pediatrics, University of Udine, Udine, Italy; fDepartment of

Pediatrics, Children’s Hospital, Paul Sabathier University, CHU

Purpan, Toulouse, France; gDepartment of Pediatrics, University

Hospital of Geneva, Geneva, Switzerland; hFirat University

Medicine Faculty, Department of Pediatrics, Division of Pediatric

Nephrology, Elazı�g, Turkey; iDepartment of Pediatrics, University

of Padova, Padova, Italy; jDepartment of Pediatrics, Medical

Center, Afula, Israel; kDepartment of Pediatrics, Cliniques

Universitaires de Mont-Godinne, Université Catholique de Louvain,

Yvoir, Belgium; lDepartment of Pediatrics, ‘‘P. and A. Kyriakou’’

Children’s Hospital, Athens, Greece; mDepartment of Pediatric

Surgery, Alexandroupolis University Hospital, Democritus

University of Thrace School of Medicine, Thrace, Greece;nDepartment of Pediatrics, Jeanne de Flandre Hospital, Lille,

France; oAkdeniz University, School of Medicine, Department of

Nuclear Medicine, Antalya, Turkey; pDepartment of Pediatrics of

Necker Hospital, Paris-Descartes University, Paris, France; andqInserm U953 Unit, Paris, France

KEY WORDS

acute pyelonephritis, children, procalcitonin, renal scarring,

urinary tract infection

ABBREVIATIONS

APN—acute pyelonephritis

AUC—area under the curve

CI—confidence interval

CRP—C-reactive protein

DCA—decision curve analysis

DMSA—dimercaptosuccinic acid

LR—likelihood ratio

OR—odds ratio

PCT—procalcitonin

ROC—receiver operating characteristic

UTI—urinary tract infection

VUR—vesicoureteral reflux

WBC—white blood cell

(Continued on last page)

870 LEROY et al at Inova Fairfax Hosp on May 27, 2013pediatrics.aappublications.orgDownloaded from

asurement and dimercaptosuccinic

acid scanning. that's a DMSA scan...

NCCPeds Journal Club - June 2013

scan this to watch

a video about PCT

and infections

Urinary tract infections (UTIs) are the

most common invasive bacterial in-

fections among young febrile chil-

dren.1 UTIs can occur as simple

bladder infections (lower UTI; bacte-

riuria only) but can also involve the

kidneys (acute pyelonephritis [APN],

in which bacteriuria is associated

with infectious renal parenchymal in-

volvement), leading to renal scarring.2

The belief that persisting APN effects

followed by late renal scarring, some-

times with recurrences, may lead to

future complications such as hyperten-

sion and/or end-stage renal failure has

been the major driving force behind the

aggressive investigation and treatment

of first-occurrence UTIs.3 The prompt

and high-quality diagnosis of APN and

differentiation from lower UTI is there-

fore of key importance. A dimercapto-

succinic acid (DMSA) scan is considered

the gold standard in imaging for both

renal parenchymal involvement during

acute infection and for late renal dam-

age left by the infection.4 However, DMSA

scans are not performed in most

children with UTI due to the limited

availability of nuclear medicine de-

partments compared with the high

number of children with UTIs. Thus,

a more practical and accessible tool

that could assist clinicians in de-

termining the presence of renal pa-

renchymal involvement and/or late

renal damage would be of great clin-

ical value.

Procalcitonin (PCT), a 116–amino acid

propeptide of calcitonin without hor-

monal activity, is an early, sensitive,

and specific marker of bacterial in-

fection.5,6 PCT is almost undetectable

under physiologic conditions or dur-

ing viral infections but rises in re-

sponse to bacterial endotoxins; the

extent of this increase seems to be

proportional to the severity of the in-

fection.6 However, its exact role, if any,

in the inflammatory response and in

the cytokine cascade remains unknown.6

In febrile UTI, the predictive ability of high

PCT concentrations for both APN and late

renal scarring has been previously in-

vestigated by several teams. A review7

and a recent systematic review and

meta-analysis8 showed that a serum

PCT .0.5 ng/mL predicts early renal

parenchymal involvement reasonably

well (diagnostic odds ratio [OR]: 14.3

[95% confidence interval (CI): 4.7–

43.2]); however, heterogeneity made

these results inconclusive.8 Moreover,

results concerning late renal scarring

were controversial, with no pooled

measurements provided.7 Most of this

heterogeneity and these discrepancies

may be due to threshold effects be-

cause the initial studies chose different

PCT cutoff values due to population

variation; unfortunately, any effects

from the latter could not be fully ex-

plored with only pooled data from the

studies. Under these circumstances,

the only way to analyze PCT as a con-

tinuous biomarker without a priori

threshold choice, simultaneously con-

trol for potential individual-level con-

founders, and then provide robust

conclusions concerning PCT as a pre-

dictor of APN and/or scarring would be

to obtain individual data unaltered by

thresholds.9

We thus aimed to perform an updated

systematic reviewandmeta-analysison

individual patient data to investigate

PCT as a predictor for both APN and

renal scarring in children with a febrile

UTI. The most appropriate threshold

values of PCT were simultaneously

studied.

METHODS

We performed a systematic review and

meta-analysis on individual patient

data, in accordance with international

standards (Centre for Reviews and

Dissemination guidelines,10 PRISMA,11

and STARD12). We electronically and

manually searched for all cohort

studies of children with UTI, a PCT

measurement, and a renal DMSA scin-

tigraphy published between January

1993 and September 2011. The search

methods are detailed in Fig 1. Ethics

committees from each participating

center approved the protocol for each

initial study from which data were

collected.

All cohort studies of consecutively in-

cluded children with a febrile UTI, a PCT

measurement, and an early (ie, within

14 days) and a late (ie, repeated at least

FIGURE 1Flow chart of the systematic review. The elec-

tronic search was conducted in Medline for all

studies of UTI with a PCT measurement in chil-

dren published from January 1993 (when PCT

was first described in relation to bacterial in-

fection5) through November 2009, and updated

in September 2011. The search strategy used

medical subject heading terms and text words,

including “procalcitonin” and “children.” The

electronic search was enhanced by hand-

searching reference lists of all included articles,

obtaining any identified articles, and also sup-

plemented by a manual review of abstracts from

the European Society of Pediatric Infectious

Diseases, the European Society for Pediatric

Nephrology, the International Pediatric Ne-

phrology Association, the American Academy of

Pediatrics, and the American Society of Ne-

phrology and by discussion with experts in the

field. The electronic search was then validated,

comparing the obtained list with the reference

list of previous reviews on PCT and UTI7,8 to

identify any potential systematic default. No

language restriction was used. The search

ended with 290 potentially eligible abstracts,

among which 19 were considered for inclusion.

One article was not included because of absence

of DMSA scan data. Finally, 18 articles were in-

cluded, representing 13 centers as follows: Afula

(Israel),28 Ahvaz (Iran),33 Antalya (Turkey),22

Athens (Greece),27 Barcelona (Spain),19,20 Elazig

(Turkey),26 Geneva (Switzerland),17,21,31 Lille

(France),25 Padova (Italy),32 Thrace (Greece),29,30

Toulouse (France),16 Udine (Italy),23 and Yvoir

(Belgium).24

ARTICLE

PEDIATRICS Volume 131, Number 5, May 2013 871

at Inova Fairfax Hosp on May 27, 2013pediatrics.aappublications.orgDownloaded from

and ER docs

still refuse

to check

for it

future complications such as hyperten-

sion and/or end-stage renal failure hassion and/or end-stage renal failure hassion and/or end-stage renal failure has

been the major driving force behind the

aggressive investigation and treatment

of first-occurrence UTIs.rst-occurrence UTIs.3

the gold standard in imaging for both

Procalcitonin (PCT), a 116–amino acid

propeptide of calcitonin without hor-

monal activity, is an early, sensitive,

and specific marker of bacterial in-

fection.fection.5,6

lots of

lit on

using this

for SBI,

etc. but

never panned

out as

practical

usuallymade byC-cells ofthyroid,but ininfection,made bylung and GIcells

seems like this has already been done,so why do it here? read on...

43.2]); however, heterogeneity made

key statistic in a meta-analysis. Ifthe main results in a bunch of smallerstudies vary too much, they can't reallybe combined well.

cause the initial studies chose different

PCT cutoff values due to population

to obtain individual data unaltered by

thresholds.thresholds.9Wow - most meta-analysesdon't get access to theactual data, but the results

**think of the study population in a meta-analysis as individual studies. Just like you need to evaluate how patients are selectedin a RCT, you need to evaluate how studies are selected to include in a meta-analysis. Was their search complete? What was inclusionexclusion criteria? Classic rookie mistakes are just searching PubMed, or searching only using keywords, or just using English articles.

tronic search was conducted in Medline for all

medical subject heading terms and text words,

youneedtoknowwhata MESHis.Checkit outthenexttimeyousearchPubMedelectronic search was enhanced by hand-

searching reference lists of all included articles,

plemented by a manual review of abstracts from

phrology and by discussion with experts in the

identify any potential systematic default. No

language restriction was used. The search

of DMSA scan data. Finally, 18 articles were in-

cluded, representing 13 centers as follows: Afula

3 months later if available) renal DMSA

scintigraphy were included. Febrile UTI

was defined as fever ($38°C) with

a positive result on bacterial urine

culture (thresholds for collection

techniques are given in Table 1). We

asked the authors to send us their

study data files (duplicates were dis-

carded if any), from which we ex-

tracted clinical (gender, age), laboratory

(C-reactive protein [CRP], PCT, white

blood cell [WBC] count), and radio-

logic (DMSA scan results, vesicou-

reteral reflux [VUR] grade13 on

cystography) data. Information con-

cerning the standard operating pro-

cedures used for urine collection, PCT

measurement, DMSA scanning (and

timing), and cystography at each

center was also collected. Methodo-

logic study quality was assessed via

a checklist (Supplemental Appendix

Tables 1 and 2).14

We analyzed the relationships between

APN/renal damage and PCT, CRP, and

WBCcount, respectively, usingdifferent,

backward, stepwise multilevel logistic-

regression models for each biomarker

(center was a group-level variable),

with fractional polynomial transfor-

mation for continuous variables if the

model assumption of linearity was vi-

olated. The discriminative ability of

each biomarker for APN and then late

renal damage was evaluated by draw-

ing receiver operating characteristic

(ROC) curves, as well as by calculating

sensitivity, specificity, predictive values,

and likelihood ratios (LR) after dichot-

omization. In addition, we compared

biomarker models by using decision

curve analysis (DCA), a method for

evaluating the clinical net benefit of

predictionmodels in which the benefits

(true-positives) are addedand theharms

(false-positives) are subtracted.15

Due to collinearity, no attempt was

made to combine biomarkers. Sta-

tistical methods are detailed in the

Appendix. TABLE

1PopulationCharacteristicsAccordingto

Each

Center

Centera

UrineCollectionTechniques

(Thresholdofthe

Positive

Bacteriuria)b

TimingofLate

DMSAScan

No.Included

(forAPN)

APN,n(%

)No.Included

(forLRS)

LRS,n(%

)Male,n(%

)Age(m

o),

Median(IQR)

All-Grade

VUR,n(%

)

Grade$3

VUR,n(%

)

Centers

using

SAorUC

Afula28

SA(any),UC(103)

—64

23(36)

0—

22(35)

14.0(4.5–23.5)

17(27)

9(14)

Antalya22

UC(104),CVM(105)

3–6mo

33

21(64)

23

4(17)

2(6)

48.0(24.0–72.0)

2(33)

2(33)

Athens2

7SA(103),UC(104),CVM(105)

6mo

61

25(41)

59

10(17)

27(44)

0.6(0.2–2.0)

9(15)

7(12)

Barcelona19,20

SA(any),UC(5.104),CVM(105)

—174

92(53)

0—

83(48)

4.8(2.0–9.0)

27(23)

9(5)

Elazig26

UC(103),CVM(105)

6mo

76

34(45)

76

12(16)

28(37)

30.0(10.5–72)

3(4)

0(0)

Geneva

17,21,31

SA(103),UC(103),CVM(105)

3mo

80

77(96)

59

34(58)

32(40)

8.9(2.4–21.5)

21(28)

9(12)

Thrace

29,30

SA(any),UC(104),CVM(105)

6mo

57

27(47)

57

12(21)

13(23)

16.0(7.0–40.0)

15(29)

11(22)

Udine23

UC(105),CVM(105)

6mo

100

63(63)

79

18(23)

31(31)

8.0(4.0–17.5)

16(18)

9(10)

Yvoir24

SA(103),UC(5.104),CVM(105)

6mo

61

48(79)

52

18(35)

13(21)

37.2(12.5–78.4)

13(22)

6(10)

Centers

usingSB

Ahvaz33

SB(105),CVM(105)

—100

62(62)

0—

19(19)

8.0(17.5–57.0)

——

Lille25

SB(105)

—42

21(50)

0—

12(29)

13.5(6.0–48.0)

14(58)

3(13)

Padova

18,32

SB(105)

12mo

72

52(72)

61

14(23)

31(43)

4.5(1.1–9.8)

13(18)

3(4)

Toulouse

16

SB(105),CVM(105)

6–24moc

91

68(75)

59

13(22)

19(21)

20.0(8.8–64.9)

32(36)

12(14)

Between-center

variability(P)

,.001

,.001

.008

.001

,.001

,.001

Total

1011

613(61)

525

135(26)

332

(33)

10.0(4.0–30.0)

182(23)

80(10)

Referencesforthearticlescorrespondingtoeach

centerare

presentedintheSupplementalAppendix.CVM,clean-voidedmidstream;IQR,interquartilerange;LRS,laterenalscars;SA,suprapubicaspiration;SB,sterilebag;UC,urethralcatheterization.

aClassifiedaccordingto

theurinecollectiontechniqueinnon–toilet-trainedchildren.

bIncolony-form

ingunitspermilliliter.

cMedianof9months.


similar

subjects,

similar

definitions,

similar

outcomes

backward, stepwise multilevel logistic-

regression models for each biomarker

with fractional polynomial transfor-

mation for continuous variables if the

model assumption of linearity was vi-

olated. The discriminative ability of

'logistic'means weshould begettingodds ratios

'stepwise'means eachvariablewas added orsubtractedfrom theregressionone at atime - sub-tractedsince it's'backward'

this is

fancy

talk for

'the data

wasn't

bell-shaped

and the

effect

non-linear

so we

changed it

for the

model &

then changed

it back

ing receiver operating characteristic

(ROC) curves, as well as by calculating

who says exciting new stats of interest to the average clinicians aren't being invented anymore?

Good

Table1!

Theindividualstudies

arethe

subjectsin

ameta-analysis.

You

canuse

this

Tableto

compare

each

study.

Try

answeringthesequestions:

-Nameastudy

thatused

abag-urine(generously&mistakenlycalleda'sterilebag')that

onlydid

earlyDMSAs?

-Nameastudy

thathad

the

highest%of

femalesAND

thehighest%of

severereflux?

Between-center

,.001

,.001

.008

.001

.001

.008

.001

.001

.008

.001

,.001

,.001

variability(P)PP

this

showswhetherthere

were

signif.

differencesbetweenstudies.

Werethere?

Due to collinearity, no attempt was

made to combine biomarkers. Sta-

blood cell [WBC] count), and radio-

(C-reactive protein [CRP], PCT, white(C-reactive protein [CRP], PCT, white

these are

the 3

things

they will

analyze

RESULTS

Study Characteristics

Following the aforementioned criteria,

weretrieved227abstractsbyelectronic

searching; 19 were potentially suitable

(Fig 1). After full text review, 1 study

was not included because of absence

of DMSA scan data, leaving 18 articles

to be included.16–33 The 13 corre-

sponding study authors were con-

tacted; all agreed to participate and

send data. A total of 1011 (97.9%)

patients fully met the inclusion criteria.

All studies had a high methodologic

quality (Supplemental Appendix Tables

1 and 2). Nine (69.2%) centers per-

formed both early and late DMSA

scans. All centers performed the early

DMSA scan within 7 days; 5 of 9 centers

performed the late scan at 6 months,

and the other centers varied between 3

and 24 months (Table 1). Among the 9

centers performing late DMSA scans,

late scanning was systematically

conducted regardless of early-scan

results in only 1 (11.1%) center (Elazig,

Turkey). Nine (69.2%) centers col-

lected urine samples following high-

quality standard operating proce-

dures (suprapubic aspiration, urethral

catheterization for non–toilet-trained

children, and clean-voided midstream

for the other patients). All centers

measured PCT by using the LUMItest

PCT immunoluminometric assay or

the BRAHMS PCT-Q semiquantitative

rapid test (BRAHMS, Hennigsdorf,

Germany). All centers included hos-

pitalized children with UTI. No ad-

verse events had been reported

in performing PCT measurement,

DMSA scanning, or cystography.

Table 1 provides details on the

characteristics of each center’s

population.

Analysis of APN and late renal scars

involved 1011 and 525 patients, re-

spectively. APNbygradewasanalyzed in

357 patients. PCT as a continuous vari-

able involved only 883 (87.3%) patients,

as PCT was measured by the PCT-Q

semiquantitative test for 128 patients.

Analysis of CRP and WBC count involved

959 (94.9%) and 962 (95.2%) patients,

respectively. VUR was examined in 772

(76.4%) patients.

Predicting APN

APN was demonstrated in 613 children

(60.6%) of the 1011 patients included.

The mean6 SD age of the children was

25.2 6 32.8 months (median: 10.5;

interquartile range: 4.3–32.3); 332

(32.8%) were boys. VUR was di-

agnosed in 182 (23.6%), and VUR $3

was found in 80 children (10.4%). PCT

as a continuous, class, or binary var-

iable was significantly associated with

APN (Table 2, Fig 2). The strength of the

association increased when the PCT

category (when ordinal variable) in-

creased (Table 2). PCT $0.5 ng/mL

(current threshold used in the litera-

ture) yielded an adjusted OR of 7.9

(95% CI: 5.8–10.9). CRP and WBC count

were also significantly related to APN,

with similar OR values for CRP as

previously described; however, lower

OR values were obtained for WBC

count (Table 2). PCT as a continuous

variable offered an area under the

ROC curve (AUC ROC) of 0.82 (95% CI:

0.79–0.84), after adjusting according

to the chosen model. The AUC ROCs for

CRP and WBC count were significantly

lower (P , .0001): 0.72 (95% CI: 0.69–

0.76) and 0.62 (95% CI: 0.57–0.65), re-

spectively, once adjusted by using the

model (Fig 3). The DCA demonstrated

that PCT provided a more statistically

robust test than CRP, WBC count, or ex-

treme systematic strategies (ie, DMSA

for all or no patients) for all threshold

probabilities. A PCT threshold $0.3 ng/

mL (median of nondiseased patient

distribution) demonstrated 88% sen-

sitivity (95% CI: 85–90), with 47%

specificity (95% CI: 42–52) (Table 2);

interestingly, PCT $0.5 ng/mL offered

a higher specificity of 72% (95% CI: 67–

76) with a 71% sensitivity (95% CI: 67–

74) (Table 3). PCT remained strongly

associated with APN when assessed

by clinical grade, as did CRP and WBC

count (Supplemental Appendix Ta-

ble 3).

Predicting Late Renal Scars

Late scars were demonstrated in 135

(25.7%) of the 525 children included.

The mean6 SD patient age was 26.66

33.8 months (median: 11.0; inter-

quartile range: 4–36); 162 (31%) were

male. VUR was present in 107 (22.0%)

of the 486 patients who underwent

cystography; VUR$3 was diagnosed in

51 (10.5%) children. PCT as a continu-

ous and binary variable was signifi-

cantly associated with renal scars

(Table 2, Fig 2). PCT$0.5 ng/mL yielded

an adjusted OR of 3.4 (95% CI: 2.1–5.7).

CRP and WBC count were also signifi-

cantly related to renal scarring (Ta-

ble 2, Fig 2). PCT as a continuous

variable resulted in an AUC ROC of 0.75

(95% CI: 0.70–0.80) once adjusted

according to the model built and was

significantly higher (P = .02) than those

values observed for CRPandWBC count

(0.70 [95% CI: 0.65–0.76] and 0.66 [95%

CI: 0.60–0.72], respectively) (Fig 3).

According to DCA, PCT was better than

CRP, WBC count, and both extreme

systematic strategies (ie, DMSA for

everyone or no one) (Fig 3). PCT $0.5

ng/mL had a 79% of sensitivity (95% CI:

71–85), with a 50% specificity (95% CI:

45–54) (Table 3).

DISCUSSION

We demonstrated that the measure-

ment of serum PCT can provide con-

siderable predictive value for the

development of APN and renal scars,

and that this predictive capacity is

better than that provided by either CRP

or WBC count regardless of considered

thresholds. Because the related medi-

cal decision process is binary (to per-

form or not to perform a DMSA scan),

our goal was to provide an alternative

ARTICLE



tacted; all agreed to participate and

send data. A total of 1011 (97.9%)

unheard ofin U.S.wherepublish orperishmakes oneprotectiveof data

Turkey). Nine (69.2%) centers col-

lected urine samples following high-

quality standard operating proce-quality standard operating proce-quality standard operating proce-

dures (suprapubic aspiration, urethral

so 4 used

low-quality

procedures

like the

'sterile'

bag

as a continuous, class, or binary var-

iable was significantly associated with

APN (Table 2, Fig 2). The strength of the

just

diffways

to

categorize

creased (Table 2). PCT 0.5 ng/mL$0.5 ng/mL

ture) yielded an adjusted OR of 7.9 diff

(95% CI: 5.8–10.9). CRP and WBC count

count (Table 2). PCT as a continuous

variable offered an area under the

ROC curve (AUC ROC) of 0.82 (95% CI:

0.79–0.84), after adjusting according

model (Fig 3). The DCA demonstrated

that PCT provided a more statistically

robust test than CRP, WBC count, or ex-

ble 2, Fig 2). PCT as a continuous

variable resulted in an AUC ROC of 0.75

(95% CI: 0.70–0.80) once adjusted

(Table 2, Fig 2). PCT$0.5 ng/mL yielded

an adjusted OR of 3.4 (95% CI: 2.1

According to DCA, PCT was better than

CRP, WBC count, and both extreme

assay that could contribute to di-

agnosis and investigate the optimal

threshold by which these clinical de-

cisions could be made. PCT$0.5 ng/mL

seemed to offer the optimal compro-

mise of sensitivity and specificity for

both APN and late renal scars: 71%

sensitivity (95% CI: 67–74) with a 72%

specificity (95% CI: 67–76) for APN;

79% sensitivity (95% CI: 71–85) with

a 50% of specificity (95% CI: 45–54)

for late scarring. Furthermore, DCA

showed that PCT offered the best

benefit/harm balance irrespective of

the chosen threshold, compared with

CRP, WBC count, or systematic strat-

egies (DMSA for everyone or no one)

for the selective identification of

children who might benefit from

a DMSA scan.

Our findings add evidence to those of

Mantadakis et al.8 Together, they sug-

gest a reasonably strong predictive

value based on PCT levels; however,

data from studies using pooled esti-

mates lead to a more cautious in-

terpretation due to the significant

heterogeneity found within these

study pools. We avoided these issues

by working with individual data,

adjusting for intercenter variability

modeling with multilevel regressions,

as well as accounting for all covari-

ables of interest at the individual level.

Moreover, the study design (a meta-

analysis of individual patient data)

allowed us to study the impact of dif-

ferent threshold levels, to perform

DCA, and to draw conclusions without

the usual threshold effect that often

affects diagnostic accuracy assessed

by meta-analysis, thus confounding

results. Our approach was comple-

mentary to that of Zaffanello et al,7

who performed a nonsystematic re-

view of the potential of PCT to predict

late renal scarring, without comput-

ing pooled estimates, as they were

confronted with different studies and

cutoffs.With our systematicmeta-analysis,

TABLE 2 Relationship Between APN or Late Renal Scars and PCT, CRP, and WBC Count

Variables Crude OR (95% CI) P Adjusted OR (95% CI) P

APN

PCT (ng/mL)a

PCT as a continuous variable 2.6 (2.2–3.1) ,.0001 2.7 (2.3–3.1) ,.0001

PCT as a class variable

,0.13 1 1

0.13–0.3 2.5 (1.3–4.9) .006 2.3 (1.2–4.6) .01

0.3–0.6 3.1 (1.6–5.8) .001 3.0 (1.6–5.6) .001

.0.6 16.9 (9.3–30.6) ,.0001 17.0 (9.3–30.9) ,.0001

PCT as a class variable

,0.5 1 1

0.5–2 3.5 (2.5–5.0) ,.0001 3.7 (2.6–5.3) ,.0001

2–10 25.3 (14.8–43.2) ,.0001 26.3 (15.3–45.0) ,.0001

.10 132.5 (17.9–979) ,.0001 146.3 (19.7–1085) ,.0001

PCT as a binary variable ($0.3) 5.5 (3.8–7.8) ,.0001 5.8 (4.0–8.3) ,.0001


CRP (mg/L)b

CRP as a continuous variable 2.7 (2.3–3.3) ,.0001 2.7 (2.3–3.3) ,.0001

CRP as a class VARIABLE

,10 1 1

10–20 2.7 (1.4–5.5) .005 2.9 (1.4–5.9) .003

20–60 8.0 (4.2–15.2) ,.0001 8.2 (4.3–15.5) ,.0001

.60 26.0 (13.6–49.7) ,.0001 26.5 (13.8–50.8) ,.0001

CRP as a binary variable ($20) 8.6 (5.5–13.3) ,.0001 8.6 (5.5–13.3) ,.0001


WBC count (cell/mm3)c

WBC count as a continuous variable 1.0 (1.0–1.0) ,.0001 1.0 (1.0–1.0) ,.0001

WBC count as a class variable

,10 000 1 1

10 000–15 000 1.8 (1.2–3.1) .01 1.8 (1.1–3.0) .01

15 000–20 000 3.1 (1.9–5.1) ,.0001 3.0 (1.8–5.0) ,.0001

.20 000 4.9 (2.9–8.3) ,.0001 4.9 (2.9–8.3) ,.0001

WBC count, binary variable ($15 000) 2.4 (1.8–3.3) ,.0001 2.4 (1.8–3.3) ,.0001

Late renal scars

PCT (ng/mL)d

PCT as a continuous variable 1.8 (1.6–2.1) ,.0001 1.7 (1.5–2.0) ,.0001


CRP (mg/L)e

CRP as a continuous variable 2.3 (1.8–2.9) ,.0001 2.2 (1.7–2.9) ,.0001



WBC count (cell/mm3)f

WBC count as a continuous variable 1.0 (1.0–1.0) ,.0001 1.0 (1.0–1.0) ,.0001

WBC count, binary variable ($15 000) 2.1 (1.4–3.3) ,.0001 2.1 (1.3–3.3) ,.0001

A model was separately derived for each biomarker (PCT, CRP, or WBC count). Univariate and multivariate analysis used

a multilevel regression model. The final multivariate model was built by using a backward stepwise reduction procedure.

Each multivariate model for each biomarker was significantly better than the reduced model based on the maximum

likelihood test (P , .05).a The analysis included 883 patients for PCTas a continuous or class variable, 937 patients for PCT dichotomized according to

the 0.3 ng/mL threshold, and 1011 patients for PCT dichotomized according to the 0.5 ng/mL threshold (because some

patients had a PCT-Q test [see text]). The final multivariate model was based on PCTand age (as a continuous variable, after

linear transformation); continuous PCTwas transformed as follows to assess the model linearity assumption: ln(PCT/100) +

3.33732173.b The analysis included 959 patients. The final multivariate model was based on CRP and age (as a continuous variable, after

linear transformation); CRP was transformed as follows to assess the model linearity assumption: ln(CRP/100) +

0.3965495066.c The analysis included 962 patients. The final multivariate model was based on WBC count and gender.d The analysis included 479 patients. The finalmultivariatemodel was based on PCTand high-grade VUR; PCTwas transformed

as follows to assess the model linearity assumption: ln(PCT/100) + 3.293186643.e The analysis included 478 patients. The final multivariate model was based on CRP and high-grade VUR; CRP was trans-

formed in the same purpose as above into: ln(CRP/100) + 0.439036841.f The analysis included 478 patients. The final multivariate model was based on WBC count and high-grade VUR.


CRP as a class VARIABLE

someone leftcaps lock on

2.6x incr for

each 1 ng/mL

PCT increase

(Reference)3.1x incr compared

to those w/ PCT

< 0.13 (the ref)

Late renal scars

APN

linear transformation); CRP was transformed as follows to assess the model linearity assumption: ln(CRP/100) +

0.3965495066. here's some specifics of transformation mentioned in methods

great caption footnotes. A table should

be able to stand on its own if plucked

out of an article and placed on the

front of USA Today.

nal multivariate model was based on WBC count and gender.

nal multivariate model was based on CRP and high-grade VUR; CRP was trans-

nal multivariate model was based on WBC count and high-grade VUR.

nalmultivariatemodel was based on PCTand high-grade VUR; PCTwas transformed

different

variables

used in the

different multivariable regressions

we offer further evidence to support

their results, updating the review in

a systematic manner and providing

pooled estimates of the predictive

ability of PCT, leading to a robust con-

clusion.

The use of imaging in this field has been

largely debated in the last decade.

However, the decision as to which tests,

if any, should be routinely conducted in

children with UTIs necessarily depends

on many factors. The “top-down” ap-

proach uses early DMSA scanning as

a screening test.34 Although children

with a negative acute-phase DMSA scan

are unlikely to develop scarring, DMSA

scans are expensive, invasive, and ex-

pose children to radiation. However,

the top-down strategy raises 2 concerns:

first, it requires DMSA scan availability

across countries and settings, which is

not currently the case, and second, the

identification of late renal scarring

results in only a more careful follow-up

of affected children. Therefore, PCT may

occupy an intermediate position useful

for identifying children at high risk for

APN and renal scarring, and for whom

a DMSA scan can be selectively pro-

posed to confirm parenchymal in-

volvement. The reported sensitivity and

specificity values may not appear very

convincing (∼70%). However, PCT is not

meant to replace DMSA scanning, which

remains the gold standard for assess-

ing parenchymal involvement (APN or

scarring). PCT could be used as an in-

termediate strategy, based on a single

biomarker, easier to set up than a nu-

clear imaging process, which can help

discriminate between lower UTI and

APN, even in settings in which DMSA

scans are not available. Interestingly,

PCT offered the best benefit/harm

balance irrespective of the chosen

threshold, compared with systematic

strategies (DMSA for everyone or no

one) for the selective identification of

children who might benefit from

a DMSA scan. Later in the imaging

evaluation, a cystography could be

proposed for children with a proven

APN, to diagnose or rule out VUR, and

treat it if necessary. Moreover, PCT

may also be helpful when choosing

between oral or intravenous antibi-

otic treatments during the early in-

fectious phase, depending on the

severity of the UTI (lower UTI or APN).

PCT could find a place in the debated

process of UTI imaging and treatment,

as a key point test in the decisional

flowchart.

There are several potential limitations

to our study that should be addressed.

First, despite the extensive electronic

and hand searches performed, a pub-

lication bias is possible, especially be-

cause test accuracy studies are more

FIGURE 2Distribution of PCT, CRP, and WBC count values according to the presence of APN or late renal scars. (A–C) Plots of PCT, CRP, and WBC count for APN. (D–F)

Plots of PCT, CRP, and WBC count for late renal scars. In each graphic, the horizontal line is the proposed threshold for dichotomizing classification via the

biomarker.

ARTICLE



Look at Graph A. What would you tell your cousin if she asked what's the PCT cutoff fordetermining whether her kid has pyleo? There's so much overlap, right? That's wherea Receiver-Operating Curve can help

There are several potential limitations

to our study that should be addressed.

good

studies

discuss

their

limit-

ations.

How many

did

they

list?

First, despite the extensive electronic

easily conducted and abandoned than

randomized controlled trials, and are

then particularly susceptible to publi-

cation bias.35 However, our current

knowledge about the precise effects of

publication bias on meta-analytic esti-

mates, as well as how to assess the

extents of these possible limitations,

are limited.36 Therefore, due to the

complexity of accurately assessing

this issue, we can provide no esti-

mates of the effect of a probable

publication bias. Secondly, a partici-

pation bias related to the response

and voluntary participation of the

centers also might be possible but

seems unlikely because all authors

contacted responded positively to our

FIGURE 3ROC curve and DCA for PCT, CRP, and WBC count. (A) ROC curves of PCT, CRP, and WBC count for APN. (B) ROC curves of PCT, CRP, and WBC count for late renal

scars. (C) The DCA of PCT, CRP, andWBC count for APN. One line represents 1 biomarker (PCT, CRP, andWBC count); the line “all” represents the benefit/harm

curve if everyone is investigated with a DMSA scan, whereas the line “none” represents the corresponding curve if no one undergoes examination.

(D) The DCA of PCT, CRP, and WBC count for late renal scars. One line represents 1 biomarker (PCT, CRP, and WBC count); the line “all” represents the

benefit/harm curve if everyone is investigated with a DMSA scan, whereas the line “none” represents the corresponding curve if no one undergoes

examination.

TABLE 3 Diagnostic Accuracy of PCT, CRP, and WBC Count for APN and Late Renal Scars

Variable Sensitivity Specificity Positive Predictive Value Negative Predictive Value Positive LR Negative LR

For APN

PCT$0.3 ng/mL 88 (85–90) 47 (42–52) 74 (71–77) 69 (63–74) 1.6 (1.5–1.8) 0.3 (0.2–0.3)

PCT$0.5 ng/mL 71 (67–74) 72 (67–76) 79 (76–83) 61 (57–66) 2.5 (2.1–3.0) 0.4 (0.4–0.5)

PCT$1 ng/mL 65 (61–69) 87 (83–90) 90 (86–92) 60 (55–64) 4.9 (3.7–6.5) 0.4 (0.4–0.5)

CRP $20 mg/L 87 (84–89) 41 (37–47) 70 (67–74) 66 (59–71) 1.5 (1.3–1.6) 0.3 (0.3–0.4)

CRP $30 mg/L 74 (70–77) 54 (49–59) 72 (69–76) 56 (51–61) 1.6 (1.4–1.8) 0.5 (0.4–0.6)

WBC count $15 000/mm3

63 (59–67) 55 (50–60) 71 (67–74) 46 (42–51) 1.4 (1.2–1.6) 0.7 (0.6–0.8)

For late renal scars

PCT$0.3 ng/mL 91 (85–95) 30 (26–35) 31 (27–36) 91 (84–95) 1.3 (1.2–1.4) 0.3(0.2–0.5)

PCT$0.5 ng/mL 88 (83–91) 50 (45–54) 51 (46–56) 87 (82–91) 1.6 (1.4–1.8) 0.4 (0.3–0.6)

PCT$1.0 ng/mL 74 (66–81) 67 (62–71) 43 (37–50) 88 (84–92) 2.3 (1.9–2.6) 0.4 (0.3–0.5)

CRP $20 mg/L 85 (78–90) 36 (32–41) 32(27–37) 87 (81–92) 1.3 (1.2–1.5) 0.4 (0.3–0.6)

CRP $30 mg/L 78 (71–85) 47 (41–52) 34 (29–39) 86 (81–90) 1.5 (1.3–1.7) 0.5 (0.3–0.7)

WBC count $15 000/mm3

68 (60–75) 51 (46–56) 33 (27–38) 82 (77–86) 1.4 (1.2–1.6) 0.6 (0.5–0.8)

Data are presented as % (95% CI).


Here are the basics of a ROC. The point closest to the upper left corner is the best cut-off for

an imperfect test. It also means the highest area under the curve (AUC)

The curve that

is highest is

best. But it

may not be

best in every

situation.

publication bias. Secondly, a partici-

requests for patient data sets. Thirdly,

the possibility of a classification bias

seems unlikely because PCT was

measured by using validated techni-

ques (immunoluminometric assay or

semiquantitative PCT-Q assay), while

blinded to the outcome. Fourthly, we

assumed that patients who had a nor-

mal DMSA had no late lesions even if

late DMSA was not performed. How-

ever, this assumption is commonplace

in the literature,37 and we verified this

assumption in the only center (Elazig,

Turkey) in which all patients system-

atically underwent both late and early

DMSA: none of the negative early DMSA

cases were followed by a positive late

DMSA. This outcome gives an in-

dication on the robustness of our

assumption. Fifth, we addressed het-

erogeneity issues due to data pooling

from different centers (including dif-

ferent time frames for the late DMSA

scan) by analyzing them as hierar-

chical data and using multilevel mod-

eling. We chose to analyze the data set

as a meta-analysis of individual pa-

tient data because this method pro-

vides the least biased and most

reliable means of addressing the

questions at hand.9 Sixth, technical

concerns, such as the collection of

urine from non–toilet-trained children

in sterile bags at 4 of the selected

centers (not a recommended method)

could have led to selection bias.38,39

This procedure could have increased

the number of false-positive results

for UTI but without consequences to

the relationship between APN or late

scarring and PCTor other biomarkers.

In addition, the inclusion of only hos-

pitalized children by the centers might

have led to another selection bias, due

to the inclusion of only the sickest

children. However, all children with

febrile UTI were systematically hospi-

talized in the included centers. Sev-

enth, the absence of previous negative

DMSA scintigraphy results might also

have introduced a selection bias. Even

if all the centers confirmed having

included exclusively or mostly chil-

dren with a first febrile UTI, it could

not guarantee that previous UTI with

persistent scarring had not occurred

in the included patients, as correct

diagnosis depends on the clinical

evaluation performed during pre-

vious febrile episodes. Moreover, not

all UTIs in children are accompanied

by fever, which is the main clinical

reason for obtaining urine cultures in

infants; therefore, previous UTIs

unaccompanied by fever may not have

been clearly identified. Lastly, the

delay between the first indications

of infection and PCT level measures

was not taken into account, and this

might have introduced a bias in the

results but only by underestimating

the relationship between APN or late

scarring and PCT, because this

marker increases as early as 6 hours

postinfection and also decreases just

as quickly at the end of infection.

CONCLUSIONS

We demonstrated that PCT has a robust

predictive ability to selectively identify

children who had APN in the early

stages of UTI and those that developed

later renal scarring. The use of serum

PCT measurements has the potential

to aid the clinical decision-making

process regarding the appropriate

acute management of children with

UTI. In particular, due to limited re-

sources and technical availability, it

may be helpful to use such an assay to

selectively identify children who may

benefit from a DMSA scan at the early

and late stages of infection. The impact

of PCT measurements on the currently

debated practice of UTI examinations

needs to be evaluated by a well-

designed impact study and may lead

to possible refinement of the de-

cisional process.

ACKNOWLEDGMENTS

The authors thank Dr Gardikis and

Dr Deftereos (Department of Pediatric

Surgery andRadiology, Alexandroupolous

University Hospital, Democritus Uni-

versity of Thrace School of Medicine,

Thrace, Greece), Dr Galetto-Lacour (De-

partment of Pediatrics, University Hos-

pital of Geneva, Geneva, Switzerland),

Dr Ellero (Department of Pediatrics,

University of Udine, Udine, Italy), Profes-

sor Da Dalt (Department of Pediatrics,

University of Padova, Italy) for par-

ticipation in data collection/sharing,

Dr Bacchetta (Department of Pediatric

Nephrology–Reference Centre for Rare

Renal Diseases, Femme Mère Enfant

Hospital, University of Lyon, Lyon, France)

for helpful discussions, and Melissa

Laird (Inserm U818, Institut Pasteur,

Paris, France) for pertinent comments

and corrections.

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(Continued from first page)

Dr Leroy designed the study, helped in collecting the data, analyzed the data, interpreted results, drafted the manuscript, and approved the article version to be

published; Drs Fernandez-Lopez, Nikfar, Romanello, Bouissou, Gurgoze, Bressan, Smolkin, Leblond, Mr Vaos, and Mr Gervaix contributed to the study design,

acquired all the data in their individual centers, revised the paper for important intellectual content, and approved the article version to be published; Drs

Tuerlinckx and Gungor contributed to the study design, acquired all the data in their centers, and revised the paper for important intellectual content; Mr

Stefanidis had great input in the conception of the study, realized the data collection in his center, performed in-depth revision of the manuscript, and approved

the article version to be published; and Ms Gendrel and Mr Chalumeau contributed to the study design and interpretation of results, critically revised the

manuscript, and approved the article version to be published.

www.pediatrics.org/cgi/doi/10.1542/peds.2012-2408

doi:10.1542/peds.2012-2408

Accepted for publication Jan 24, 2013

Address correspondence to Sandrine Leroy, MD, PhD, Centre for Statistics in Medicine, University of Oxford, Wolfson College Annexe, Linton Rd, Oxford OX2 6UD,

United Kingdom. E-mail: [email protected]

PEDIATRICS (ISSN Numbers: Print, 0031-4005; Online, 1098-4275).

Copyright © 2013 by the American Academy of Pediatrics

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

FUNDING: Dr Leroy was funded by a postdoctoral fellowship grant from the French Society of Nephrology and from the Fondation Bettencourt.

ARTICLE



X-Axis is 'Threshold Probability' - abbreviated Pt

It's the probability of disease derived from a test that a clinician would act

i.e. cut out a tumor, do a bronch, admit for IV antibiotics, etc.

Ranges from 0 to 100%

y-axis

is 'Net benefit'

True Positives

minus

(False Positives

* Pt/(1-Pt)

0if you did nothing no matter what the test result is, the net benefit is 0 no matter what

you can translatethe net benefit 0.Yas 'I can identifyY patients w/ a diseaseby testing 100 ptsand not treating anyoneunnecessarily

treating

everyone no

matter what the

test is looks like the

pink line.

Here are the basic components of a DCA curve: the x and y axis, and the 2 standard curves

(treat everyone, treat noone). The results of the test are plotted with these.

0.3

0.2

0.1

-0.1

-0.2

-0.3

The authors have indicated they have no financial relationships relevant to this article to disclose.

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