MALIK ALQUB MD. PHD. Clinical labrotaries. Steps in the Investigation of a Patient Patient History...

MALIK ALQUB MD. PHD. Clinical labrotaries

Steps in the Investigation of a Patient Patient History Physical Examination Laboratory Tests Imaging Techniques Diagnosis Therapy Evaluation

Medical testing Laboratory Testing Doctor requires information Patient sample collection Sample received & processed Computer system maintenance Report generation

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)

Laboratory Medicine, disciplines. Clinical biochemistry Histopathology Microbiology Cytology Blood Transfusion Immunology Haematology Virology

CLINICAL CHEMISTRY Measurement of amounts of specific elements transported in the biological samples oProteins oSugars oCellular breakdown products oHormones oToxins oEtc...

Biochemical tests Are divided in three main categories Core biochemistry Urgent tests Specialized tests

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

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

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

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

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

Pre analysis

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

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

Sampling/Conditions Collection Tubes Timing, 24hr urine collection, dynamic tests Temperture. cryoglobulines

Collection Tubes (Vacutainers) Serum Separator Tube (SST) Separator Gel Serum Clot

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

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

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

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

Collection tubes Blue-top tubes contain sodium citrate, which chelates calcium and inhibits coagulation Used for coagulation studies because it is easily reversible.

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)

Transport The delay between sample collection and analysis, the conditions of transport must be respected like Time (blood gases) Temperature (fasting glucose)

Reception and identification Was the blood collected from the correct patient? Was the blood correctly labeled? Patient name, ID, date, time of collection, phlebotomist

Conform to laboratory guidance Look if the condition of Pre analysis are respected Patient name, ID, date, time of collection Volume

Pretreatment of biological samples Centrifugation Dilution Storage

Analysis Quantitative and qualitative measurements Quantitative, Na, K, total Protein, etc Qualitative, Protein electrophoresis of urine

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

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

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.

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

Continuous Flow

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.

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

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.

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.

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.

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.

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

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

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

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 !

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 !!!

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

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

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

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

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 !!!

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

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 )

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.

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

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

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

Performance of a test

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 )

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

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 )

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 %

Performance of a test TN Sp = TN + FP TP Se = TP + FN Disease Test FP TN TP FN NoYes +

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

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:

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

Effect of Decreasing the Threshold Disease Test FP TN TP TN Sp = TN + FP FN NoYes TP Se = TP + FN +

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

TP Se = TP + FN TN Sp = TN + FP Effect of Increasing the Threshold Disease Test FP TN TP FN NoYes +

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?

When false diagnosis (FP) is worse than missed diagnosis (FN) Example: Screening for congenital toxoplasmosis One should minimise false positives Prioritise SPECIFICITY

When missed diagnosis (FN) is worse than false diagnosis (FP) Example: Testing for Helicobacter pylori infection One should minimise the false negatives Prioritise SENSITIVITY

MALIK ALQUB MD. PHD. Clinical labrotaries. Steps in the Investigation of a Patient Patient History...

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