MALIK ALQUB MD. PHD. Clinical labrotaries. Steps in the Investigation of a Patient Patient History Physical Examination Laboratory Tests Imaging Techniques.
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Slide 1
MALIK ALQUB MD. PHD. Clinical labrotaries
Slide 2
Steps in the Investigation of a Patient Patient History
Physical Examination Laboratory Tests Imaging Techniques Diagnosis
Therapy Evaluation
Slide 3
Medical testing Laboratory Testing Doctor requires information
Patient sample collection Sample received & processed Computer
system maintenance Report generation
Slide 4
Laboratory Medicine A discipline of medicine that functions to
provide diagnostic tests which are utilized by physicians to assess
the health of an individual. Must more than just a service. Dynamic
interaction with all hospital departments (Emergency (ER),
Intensive Care Unit (ICU), Cardiac Care Unit (CCU) as well as
physicians outside of the hospital to maximize health care through:
Consultation regarding tests to be requested Education Medical
students, residents Medical Technologists Medical Staff
Development, Evaluation and Implementation of New Diagnostic Assays
Supporting Clinical and Basic Research Interaction with all
departments to maintain and/or improve the flow and accuracy of
information (i.e test results)
CLINICAL CHEMISTRY Measurement of amounts of specific elements
transported in the biological samples oProteins oSugars oCellular
breakdown products oHormones oToxins oEtc...
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Biochemical tests Are divided in three main categories Core
biochemistry Urgent tests Specialized tests
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Core Lab Many tests where abnormal values are incompatible with
life and therefore of critical value to the physician Electrolytes:
sodium (Na), potassium (K), Chloride (Cl) Blood gases: pO 2, pCO 2,
pH, HCO3, oxygen saturation Endocrine: Thyroid hormones Cardiac
markers Liver enzymes Amylase Glucose Toxicology Ethanol, methanol
Drugs of abuse generally conducted as a screen
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Uregent tests Done on emergency basis Continous service 24/24hr
Electrolytes: sodium (Na), potassium (K), Chloride (Cl) Blood
gases: pO 2, pCO 2, pH, HCO3, oxygen saturation Cardiac markers
Glucose
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Specilized tests Hormones Specific proteins Vitamins Drugs
Lipids DNA analysis Rare tests Only larger centres have Special
Chemistry Lab because Requires the volume of specimens to justify
the test High cost of equipment to relative few specific tests
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Specilized test Instrumentation and Analytical Methods
Electrophoresis Used to separate serum proteins into 5 distinct
bands Used to separate Lipoproteins into 4 distinct bands Often
used to separate isoforms of enzymes HPLC Used to measure vitamins
and hemoglobin variants Infrared Spectroscopy Used to analyze
components of Kidney stones Radioimmunoassay (RIA) Used less and
less but still employed for those analytes present in minute
amounts (pmol) in the blood (ie. testosterone) GC-MS (Gas
chromatography-mass spectroscopy) and/or LC-MS (liquid
chromatography- mass spectroscopy. Used for quantitative drug
measurement
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Analysis of biological samples Pre analysis Prescription
Biological samples Sampling/Conditions Transport Reception and
identification Conform to laboratory guidance Pretreatment of
biological samples Analysis Quantitative and qualitative
measurements Automation Control of quality Post analysis Technical
validation Biological validation Reporting interpretation
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Pre analysis
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Prescription Good communication between medical staff and lab
To avoid unnecessary tests To precise the condition of sampling To
inform the medical staff of scheduled analysis To Aid in
interpretation of results To prepare the lab in critical
situation
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Biological samples Blood Urine Cerebrospinal Fluid Amniotic
Fluid Duodenal Aspirate Gastric Juice Gall stone Kidney Stone
Stools Saliva Synovial Fluid milk Tissue Specimen Comprise the
majority of all specimens analyzed
Collection tubes Gold (and tiger) top tubes contain a gel that
forms a physical barrier between the serum and cells after
centrifugation No other additives are present Gel barrier may
affect some lab tests
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Collection tubes Used for Glucose measurement. After blood
collection, glucose concentration decreases significantly because
of cellular metabolism Gray-top tubes contain either: Sodium
fluoride and potassium oxalate, or Sodium iodoacetate Both
preservatives stabilize glucose in plasma by inhibiting enzymes of
the glycolytic pathway NaF/oxalate inhibits enolase Iodoacetate
inhibits glucose-3-phosphate dehydrogenase
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Collection tubes Green-top tubes contain either the Na, K, or
lithium (Li) salt of heparin. Most widely used anticoagulant for
chemistry tests. Should not be used for Na, K or Li measurement Can
effect the size and integrity of cellular blood components and not
recommended for hematology studies Heparin accelerates the action
of antithrombin III, which inhibits thrombin, so blood does not
clot (plasma) The advantage of plasma is that no time is wasted
waiting for the specimen to clot
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Collection tubes Lavender-top tubes contain the K salt of
ethylenediaminetetraacetic acid (EDTA), which chelates calcium
(essential for clot formation) and inhibits coagulation Used for
hematology, and some chemistries Cannot be used for K or Ca
tests
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Collection tubes Blue-top tubes contain sodium citrate, which
chelates calcium and inhibits coagulation Used for coagulation
studies because it is easily reversible.
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Collection tubes Brown and Royal Blue top tubes are specially
cleaned for trace metal studies Brown-top tubes are used for lead
(Pb) analysis Royal blue-top tubes are used for other trace element
studies (acid washed)
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Transport The delay between sample collection and analysis, the
conditions of transport must be respected like Time (blood gases)
Temperature (fasting glucose)
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Reception and identification Was the blood collected from the
correct patient? Was the blood correctly labeled? Patient name, ID,
date, time of collection, phlebotomist
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Conform to laboratory guidance Look if the condition of Pre
analysis are respected Patient name, ID, date, time of collection
Volume
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Pretreatment of biological samples Centrifugation Dilution
Storage
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Analysis Quantitative and qualitative measurements
Quantitative, Na, K, total Protein, etc Qualitative, Protein
electrophoresis of urine
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Automation Why automation; Increase the number of tests by one
person in a given period of time Minimize the variations in results
from one person to another Minimize errors found in manual analyses
equipment variations pipettes Use less sample and reagent for each
test
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Types Of Analyzers Continuous Flow Tubing flow of reagents and
patients samples Centrifugal Analyzers Centrifuge force to mix
sample and reagents Discrete Separate testing cuvets for each test
and sample Random and/or irregular access
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Continuous Flow This first AutoAnalyzer (AA) was a
continuous-flow, single-channel, sequential batch analyzer capable
of providing a single test result on approximately 40 samples per
hour. Analyzers with multiple channels (for different tests),
working synchronously to produce 6 or 12 test results
simultaneously at the rate of 360 or 720 tests per hour.
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In continuous flow analyzers, samples were aspirated into
tubing to introduce samples into a sample holder, bring in reagent,
create a chemical reaction, and then pump the chromagen solution
into a flow- through cuvette for spectrophotometric analysis. C
ONTINUOUS F LOW
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Continuous Flow
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Continuous flow is also used in some spectrophotometric
instruments in which the chemical reaction occurs in one reaction
channel and then is rinsed out and reused for the next sample,
which may be an entirely different chemical reaction.
Slide 35
Centrifugal Analyzers Discrete aliquots of specimens and
reagents are piptted into discrete chambers in a rotor The
specimens are subsequently analyzed in parallel by spinning the
rotor and using the resultant centrifugal force to simultaneously
transfer and mix aliquots of specimens and reagents into radially
located cuvets. The rotary motion is then used to move the cuvets
through the optical path of an optical system
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Discrete analyzers Discrete analysis is the separation of each
sample and accompanying reagents in a separate container. Discrete
analyzers have the capability of running multiple tests on one
sample at a time or multiple samples one test at a time. They are
the most popular and versatile analyzers and have almost completely
replaced continuous-flow and centrifugal analyzers.
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Discrete Analyzers Sample reactions are kept discrete through
the use of separate reaction cuvettes, cells, slides, or wells that
are disposed of following chemical analysis. This keeps sample and
reaction carryover to a minimum but increases the cost per test due
to disposable products.
Slide 38
What is Quality Control? Quality Control in the clinical
laboratory is a system designed to increase the probability that
each result reported by the laboratory is valid and can be used
with confidence by the physician making a diagnostic or therapeutic
decision.
Slide 39
CONTROL OF QUALITY What is normal or OK What makes something
weird, abnormal or deviant and something to worry about? When does
a laboratory test result become weird or abnormal ? At some point
we have to draw a line in the sand on this side of the line youre
normal on the other side of the line youre abnormal. Where and how
do we draw the line ? In the laboratory, we have to be concerned
with these issues because we have to give meaning to our
observations or test results Are they normal or abnormal ?
Statistics is used to draw lines in the sand for patient specimens,
control specimens and calibrators If the results are normal we re
comfortable about them and dont worry But if theyre abnormal, were
uncomfortable and we fear that there is something wrong with the
patient or the test procedure.
Slide 40
To answer these questions we need a little statistics There are
two main ideas we need to concern ourselves with Central tendency
how numerical values can be expressed as a central value )
Dispersal about the central value ( how spread out are the numbers
? ) Using these two main ideas we can begin to understand how basic
statistics are used in clinical chemistry to define normal values
and when our instruments are ( or are not ) generating expected
numerical results
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Common Descriptive techniques MeanAverage value (expression of
central tendency ) MedianMiddle observation (expression of central
tendency ) ModeMost frequent observation (expression of central
tendency ) Observations can be grouped together into smaller
groups. The frequency of each smaller group can be expressed
graphically as a bar-chart or histogram Standard Deviation ( SD ) :
mathematical expression of the dispersion of the observations how
spread out the observations are from each other Coefficient of
Variation ( CV ) : A way of expressing the Standard Deviation in
terms of the average value of the observations that were used in
its calculation
Slide 42
Accuracy versus Precision Accuracy Accuracy : Observations that
are close to the true or correct value Precision Precision :
Observations that are reproducible or repeatable The laboratory
must produce results that are both accurate and reproducible
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Right on target ! Close enough ? In the laboratory we need to
report tests with accuracy and precision, but how accurate do we
need to be? Its not possible to hit the bulls-eye every time. So
how close is close enough? target !
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3 possible testing outcomes - Hitting the target x x x x x x
Lacks precision and accuracy x x x x x x Has good precision but
poor accuracy x x x Good precision and good accuracy A lab must
report the correct results all the time !!!
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Formulas for Statistical Terms Mean = Median : List all the
observations in order of magnitude and pick the observation thats
in the middle Odd # of observations = Middle observation Even # of
observations = Average of the 2 middle values Mode : The
observation that occurs most frequently There may be more than one,
or none at all All three of these are expressions of a central
observation, but they dont say anything about the observations as a
whole Are they close together? Although we can look at all the
individual observations, the mean, median and modes by themselves
do not give us any indication about the dispersion of the
observations
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Formulas for Statistical Terms Standard Deviation : n = the
number of observations (how many numerical values ) = the sum of in
this case, the sum of all the = the mean value X = the value of
each individual observation this means that the value of will have
to be calculated for every value of x The Standard Deviation is an
expression of dispersion the greater the SD, the more spread out
the observations are
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Discussion of the Standard Deviation (SD) As the name suggests,
it is a measurement of deviation More specifically, it can measure
deviation in terms of an individual observation or a group of
observations In both cases, the deviation is measured in terms of
how far away the observation(s) are from the mean value The SD is a
measurement of dispersion We use the SD to draw our lines in the
sand For most considerations, laboratories will define normal or
acceptable results as being within 2.0 SD from the mean value If
results are greater than 2.0 SD from the mean, then we say they are
abnormal or out of control This means that they are unlike the
other observations and they may be the results of faulty laboratory
testing
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Example of the Standard Deviation Establishment of Normal
Values: A minimum of 20 observations should be sampled in order to
obtain valid results ( but Ill use just 6 to save time ) Lets
determine the normal range for fasting plasma glucose using 6
people: Johns glucose = 98 mg/dlAverage = 109 mg/dl Pauls glucose=
100 mg/dlSD = 20.0 mg/dl Georges glucose= 105 mg/dl2 SD = 40.0
mg/dl Ringos glucose = 150 mg/dl Micks glucose = 102 mg/dl Erics
glucose = 101 mg/dl That means that the normal range for this group
is from 109 40, or 69 - 149 which is 2.0 SD from the mean Ringo is
considered abnormal if we use this commonly accepted criteria to
define normal and abnormal By the way, the CV for this group of
observations is about 18% - a fairly big dispersal about the
mean
Slide 49
Example of Control Specimens Every test in the laboratory
requires that control specimens be performed on a regular basis to
ensure the testing process is producing accurate results. Running
controls is a check on the lab techs technique, reagents and
instrumentation. Suppose you are running the glucose normal control
and you get the following results : Glucose = 100 mg/dl Is this
acceptable? To answer this question you need to known what the
previously established acceptable range for this control specimen
is ( done like the normal range ) Lets say the acceptable range for
this control specimen is : Mean = 104 mg/dl SD = 5 mg/dl That means
that 2.0 SD = 10 mg/gl The acceptable range for this control is 104
10 = 94 - 104 mg/dl In other words, 94 104 mg/dl is considered to
be acceptable dispersion. So, your control value of 100 mg/dl is
well within the acceptable range. Everything seems to be working OK
!!!
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Example of a Levy Jennings chart A Levy Jennings chart is a
graph that plots QC values in terms of how many Standard Deviations
each value is from the mean
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But what if your control specimen is out of control? Out of
control means that there is too much dispersion in your result
compared with the rest of the results its weird This suggests that
something is wrong with the process that generated that observation
Patient test results cannot be reported to physicians when there is
something wrong with the testing process that is generating
inaccurate reports Patient test results cannot be reported to
physicians when there is something wrong with the testing process
that is generating inaccurate reports Remember No information is
better than wrong information Remember No information is better
than wrong information Things that can go wrong and what to do
Instrumentation malfunction ( fix the machine ) Reagents
deteriorated, contaminated, improperly prepared or simply used up (
get new reagents) Tech error ( identify error and repeat the test )
Control specimen is deteriorated or improperly prepared ( get new
control )
Slide 52
Formulas for Statistical Terms Coefficient of Variation (CV) %
= The CV allows us to compare different sets of observations
relative to their means Why the CV? Whats wrong with the SD? Each
SD is a reflection only of the data that produced it, and not other
groups of observations. You cant use the SD to compare different
groups of data because they are measuring different observations -
you cant compare apples to oranges. The CV can turn all groups of
observations into a percentage of their relative means - everything
gets turned into oranges.
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Example of the usefulness of the CV Which of the 2 following
data sets is the more precise, or has the least dispersion? Set ASD
= 1.0 Set BSD = 2.0 Simple, right? It seems to be Set A because it
has the lower SD Remember, however, that we dont know what was
measured. Heres some more information The mean for Set A = 10 The
SD is 1/10 of the mean The mean for Set B = 1000 The SD is 1/500 of
the mean In spite of having a larger SD, Set B is actually far more
precise in terms of the relative size of its observations
Slide 54
Establishment of Reference Ranges Each lab must establish its
own reference ranges Factors affecting reference ranges Age Sex
Diet Medications Physical activity Pregnancy Personal habits (
smoking, alcohol ) Geographic location ( altitude ) Body weight
Laboratory instrumentation ( methodologies ) Laboratory reagents
Normal ranges ( reference ranges ) are defined as being within 2
Standard Deviations from the mean
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Why Analytical Results Vary Inter-individual Variation Age Sex
Race Genetics Long term health status Pre-analytical Variation
Transport Exposure to UV light Standing time before separation of
cells Centrifugation time Storage conditions Intra-individual
Variation Diet Exercise Drugs Sleep pattern Posture Time of
venipucture Length of time tourniquet is applied Analytical
Variation Random errors Systematic errors Post-analytical
Transcriptions errors Results reported to wrong patient
Slide 56
Performance of a test
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Sensitivity of a test Ability of a test to identify correctly
affected individuals proportion of people testing positive among
affected individuals True patients (gold standard) Test ++ True
positive (TP) False negative (FN) Sensitivity (Se) = TP / ( TP + FN
)
Slide 58
Sensitivity of a PCR for congenital toxoplasmosis Sensitivity =
54 / 58 = 0.931= 93.1 % Patients with toxoplasmosis Rapid test True
positive54 False negative 4 58
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Specificity of a test Ability of test to identify correctly
non-affected individuals - proportion of people testing negative
among non-affected individuals False positive (FP) True negative
(TN) Test Non-affected people ++ Specificity (Sp) = TN / ( TN + FP
)
Slide 60
Specificity of a PCR for congenital toxoplasmosis Individuals
without toxoplasmosis Rapid test False positive 11 True negative114
125 Specificity= 114 / 125 =0.912 = 91.2 %
Slide 61
Performance of a test TN Sp = TN + FP TP Se = TP + FN Disease
Test FP TN TP FN NoYes +
Slide 62
Distribution of quantitative test results among affected and
non-affected people (ideal case) 0 5 10 15 20 Quantitative result
of the test TN Non affected: Affected: TP Number of people tested
Threshold for positive result
Slide 63
Distribution of quantitative results among affected and
non-affected people (realistic case) 0 5 10 15 20 TNTP FNFP
Non-affected: Threshold for positive result Quantitative result of
the test Number of people tested Affected:
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Effect of Decreasing the Threshold TN TP FN FP Non affected:
Affected: Threshold for positive result Number of people tested
Quantitative result of the test 0 5 10 15 20
Slide 65
Effect of Decreasing the Threshold Disease Test FP TN TP TN Sp
= TN + FP FN NoYes TP Se = TP + FN +
Slide 66
0 5 10 15 20 TN TP FN FP Non-affected: Affected: Threshold for
positive result Number of people tested Quantitative result of the
test Effect of Increasing the Threshold
Slide 67
TP Se = TP + FN TN Sp = TN + FP Effect of Increasing the
Threshold Disease Test FP TN TP FN NoYes +
Slide 68
Performance of a Test and Threshold Sensitivity and specificity
vary in opposite directions when changing the threshold The choice
of a threshold is a compromise to best reach the objectives of the
test consequences of having false positives? consequences of having
false negatives?
Slide 69
When false diagnosis (FP) is worse than missed diagnosis (FN)
Example: Screening for congenital toxoplasmosis One should minimise
false positives Prioritise SPECIFICITY
Slide 70
When missed diagnosis (FN) is worse than false diagnosis (FP)
Example: Testing for Helicobacter pylori infection One should
minimise the false negatives Prioritise SENSITIVITY