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S TEP BY S TEP A PPROACH TO E VALUATION AND C OMPARISON OF A NALYTICAL M ETHODS J M KUYL Department of Chemical Pathology NHLS Universitas & UFS.

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Presentation on theme: "S TEP BY S TEP A PPROACH TO E VALUATION AND C OMPARISON OF A NALYTICAL M ETHODS J M KUYL Department of Chemical Pathology NHLS Universitas & UFS."— Presentation transcript:

1 S TEP BY S TEP A PPROACH TO E VALUATION AND C OMPARISON OF A NALYTICAL M ETHODS J M KUYL Department of Chemical Pathology NHLS Universitas & UFS

2 Physicists have a long tradition of building their own equipment, and are often fascinated by its mechanics. Biologists’ fascination is primarily with the mechanics of nature and, for many, the machines themselves are simply tools – complicated ‘black boxes’ that produce the results they need. It doesn’t help that the tools biologists are using may have been designed by physicists, and that the two groups tend to use different jargon. Nature 2007; 447: 116

3 I NTRODUCTION Quantitative analytical methods have become more reliable and more standardized. Emphasis moved away from methods development to the selection and evaluation of those commercial available methods that suit a particular laboratory best. Commercial kit methods are ready for implementation in the laboratory, often in a “closed” analytical system on a dedicated instrument. Furthermore, method evaluation is a costly exercise in terms of reagents, specimens, and labour and time of the professionals doing the evaluating. If not done properly it wastes laboratory revenue and time, if the method is accepted might lead to errors in medical decisions based on results the method generates on patient samples.

4 Generally what happens is that laboratories are most concerned with getting the methods up and running that there is little time, or thought given, to selection and evaluation studies.

5 The most common scenario is the implementation of readily available commercial kit methods, often in a “closed” analytical system on a dedicated instrument. When a new clinical analyzer is included in the overall evaluation process, various instrumental parameters also require evaluation. Information on most of these parameters should be available from the instrument manufacturer, who should also be able to furnish information on what user studies to conduct in estimating these parameters for an individual analyzer.

6 Establish need Method selection Definition of quality goal Method evaluation Method development Implementation Routine analysis Quality control practices Submission of specimen Result report

7 Reasons for Selecting a New Method improve accuracy and / or precision over existing methods to reduce reagent cost to reduce labour cost new analyzer or instrument to measure a new analyte

8 M ETHOD S ELECTION Evaluation of need Application characteristics Method characteristics Analytical performance characteristics

9 Scopes of Method Evaluation Studies Evaluation is the determination of the analytical performance characteristics of a new method. Validation is confirmation by examination and provision of objective evidence that the particular requirements for a specific intended use can be consistently fulfilled. Verification is confirmation by examination of objective evidence that specified requirements have been fulfilled. Demonstration is a minimum evaluation for a laboratory to use to show that it is able to obtain expected results by following the manufacturer’s instructions. This is appropriate for test systems whose performance characteristics have been well studied and documented.

10 Method Evaluation and Validation Main purpose is error assessment. To demonstrate that prior to reporting patient test results, it can obtain the performance specifications for accuracy, precision, and reportable range of patient test results, comparable to those established by the manufacturer. The laboratory must also verify that the manufacturer’s reference range is appropriate for laboratory’s population.

11 An Overview of Qualitative Terms and Quantitative Measures Related to Method Performance Qualitative ConceptQuantitative Measure Trueness Closeness of agreement of mean value with “true value” Bias A measure of the systematic error Precision Repeatability (within run) Intermediate precision (long term) Reproducibility (interlaboratory) Imprecision (sd) A measure of the dispersion of random errors Accuracy Closeness of agreement of a single measurement with “true value” Error of measurement Comprises both random and systematic influences

12 Total Analytical error TE A. RE SE TE A = RE + SE TE A

13 Constant and proportional errors.

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15 Analytical Sensitivity Several terms describe the different aspects of the minimum analytical sensitivity of a method. Limit of absence (LoA) is the lowest concentration of analyte that the method can differentiate from zero. Limit of detection (LoD) is the minimum concentration of analyte whose presence can be quantitatively detected under defined conditions. Functional sensitivity or limit of quantification (LoQ) is the minimum concentration of analyte whose presence can be quantitatively measured reliably under defined conditions. The concentration at which the CV = 20%.

16 Illustration of different aspects of analytical sensitivity or detection limits.

17 Random Analytical Error (RE) Factors contributing to random analytical error (RE) are those that affect the reproducibility of measurement. These include: instability of the instrument, variations in the temperature, variations in the reagents and calibrators (and calibration- curve stability), variability in handling techniques such as pipetting, mixing, and timing, and variability in operators. These factors superimpose their effects on each other at different times. Some cause rapid fluctuations, and others occur over a longer time. Thus RE has different components of variation that are related to the actual laboratory setting.

18 Random Analytical Error (RE) Components Within-run component of variation (  wr ) Within-day, between-run variation (  br ) Between-day component of variation (  bd )

19 Within-run component of variation (  wr ) is caused by specific steps in the procedure: 1.sampling 2.pipetting precision 3.short-term variations in temperature and 4.stability of the instrument.

20 Within-day, between-run variation (  br ) is caused by: 1.instability of calibration curve 2.differences in recalibration that occur throughout the day, 3.longer term variations in the instrument, 4.small changes in the condition of the calibrator and reagents, 5.changes in the condition of the laboratory during the day, and 6.fatigue of the laboratory staff.

21 Between-day component of variation (  bd ) is caused by: 1.daily variations in the instrument, 2.changes in calibrators and reagents (especially if new vials are opened each day), and 3.changes in staff from day to day. 4.Although not a true random component of variation, any drift in the stability of the calibration curve over time greatly affects the  bd as well.

22 Total Variance of a Method (  t 2 )  t 2 =  wr 2 +  br 2 +  bd 2 RE =  t

23 Familiarization with the method It is essential that operators of the method become thoroughly familiar with the details of the method and instrument operation before the collection of any data that will be used to characterize the method’s performance. May include training by the manufacturer. It should be of sufficient duration that, at its completion, the operators can perform all aspects of the method or instrument operation comfortably.

24 Experiments for Estimating Analytical Errors

25 Outliers The importance of daily examination and plotting of comparison-of-method data cannot be over emphasized, and the data must be carefully examined for outliers. Definition of an outlier from a regression line: | y i – Y i | > 4s x,y Outlier specimens must be detected immediately and reanalyzed by both methods so that the data can correct or confirm the outlier.

26 An example evaluation study: Cholesterol in serum. Step 1: Analytical needs Rapid procedure with a turnaround time of  30 min suitable for lipid clinic requirement. Short turnaround time means that patients do not have to come back for treatment based on lipid-profile results. A sample volume of  200 µL. Analytical range of 0 to 20 mmol/L. High through-put. Analytical goals

27 An example evaluation study: Cholesterol in serum. Analytical Goals AnalyteAcceptable performance criteria (CLIA 88) Decision level X C Allowable error (CLIA 88) Maximum sd (CLIA 88) (CV%) Medically based maximum sd (Fraser) (CV%) Albumin± 10%35 g/L (2.6%)0.5 (1.43%) Cholesterol± 10%5.2 mmol/L (2.5%)0.14 (2.7%) Creatinine± 15% 88 µmol/L 265 µmol/L (8%) 9.7 (3.7%) 1.8 (2.0%) 6.2 (2.3%) Glucose± 10% 2.75 mmol/L 6.9 mmol/L 11.0 mmol/L (2.9%) 0.18 (2.6%) 0.28 (2.55%) 0.06 (2.2%) 0.15 (2.2%) 0.24 (2.2%) Hb A 1C 7.0%0.35%0.14% K± 0.5 mmol/L 3.0 mmol/L 6.0 mmol/L (4%) 0.12 (2%) 0.07 (2.33%) 0.14 (2.33%) ALP± 30%150 U/L4511 (7.3%)5.1 (3.4%) CK± 30%200 U/L6015 (7.5%)40 (20%)

28 An example evaluation study: Cholesterol in serum. Step 2: Quality goals Medical decision (X C ) levels of interest for cholesterol analysis are taken as 4.5 mmol/L; levels below this indicate low risk of CVD, and 6.0 mmol/L; high risk, levels above this should be actively treated with cholesterol lowering drugs, respectively. Precision goals for cholesterol are defined to be 0.12 mmol/L at 4.5 mmol/L and 0.15 mmol/L at 6.0 mmol/L (2.5%). Total error goals (TE A ) are 0.45 mmol/L at 4.5 mmol/L and 0.60 mmol/L at 6.0 mmol/L (10%).

29 Total Analytical error. ( TE A ) TE A = 10% RE = 2.5% SE = 7.5% For Cholesterol TE A = RE + SE 10% = 2.5% + 7.5%

30 An example evaluation study: Cholesterol in serum. Step 3: Method selection Existing laboratory analyzer Beckman-Coulter LX20 analyzer Cholesterol kit specifically designed for this analyzer. Senior operator who is familiar with this particular analyzer and is available to do the evaluation.

31 An example evaluation study: Cholesterol in serum. Step 4: Test material selection QC-material Synchron 1: mean [cholesterol] 2.71 mmol/L, Synchron 2: mean [cholesterol] 4.19 mmol/L, and Synchron 3: mean [cholesterol] 5.82 mmol/L. Pooled patient serum two levels A and B – matrix closest to real patient serum. 20 Patient serum samples to be run in parallel with existing laboratory method.

32 An example evaluation study: Cholesterol in serum. Step 5: Within-run imprecision Performed by analyzing 6 aliquots of Synchron 1, 2, and 3 and Pool A and B within a run. Results: Mean (mmol/L)sd (mmol/L) RE % Synchron Synchron Synchron Pool A Pool B

33 An example evaluation study: Cholesterol in serum. Step 5a: Within-run imprecision Testing for acceptable performance RE against Maximum allowable CV% CLIA 88: 2.5% > synchron 1: 1.04% < Fraser: 2.7% CLIA 88: 2.5% > synchron 2: 1.00% < Fraser: 2.7% CLIA 88: 2.5% > synchron 3: 1. 26% < Fraser: 2.7% CLIA 88: 2.5% > pool A: 1.17% < Fraser: 2.7% CLIA 88: 2.5% > pool B: 1.67% < Fraser: 2.7% proceed with step 5b

34 An example evaluation study: Cholesterol in serum Step 5b: Within-run imprecision Testing for acceptable performance RE against TE A If 4 x RE > TE A reject method If 4 x RE < TE A proceed with step 6 With the TE A = 10% for cholesterol, the within-run imprecision of synchron 1, 2, 3 and pool A and B each passes the test. Proceed to step 6.

35 An example evaluation study: Cholesterol in serum. Step 6: Between-run (day-to-day) precision Performed by analyzing aliquots of pool A and B for 20 days Results Mean (mmol/L) sd (mmol/L) RE % 4 x RE% Pool A < < 10 Pool B < < 10

36 An example evaluation study: Cholesterol in serum Step 7: SD has confidence intervals Factors for computing one-sided confidence intervals for standard deviation. Degrees of freedom (N – 1) A 0.05 A

37 An example evaluation study: Cholesterol in serum Step 7: Confidence-interval estimate of random error RE U and RE L ; N = 20 Mean (mmol/L) sd (mmol/L) sd U = sd x A.95 sd L = sd x A.05 RE U = 4 x sd U RE L = 4 x sd L Pool A Pool B RE U pool A > and RE U pool B > 0.649

38 An example evaluation study: Cholesterol in serum Step 8: Validation of linearity or reportable range Obtained pool C by combining all serum samples with [cholesterol] > 15 mmol/L. Prepared the following samples: Sample 1 Special prepared with [cholesterol]  0 Sample 23 parts sample part pool A Sample 3Pool A Sample 4Pool B Sample 52 parts sample parts pool C Sample 6Pool C

39 An example evaluation study: Cholesterol in serum Step 8: Validation of linearity or reportable range Pools analyzed by Kendal-Abell method (reference method) [cholesterol] mmol/L Pool A4.88 Pool B6.52 Pool C16.7

40 An example evaluation study: Cholesterol in serum Step 8: Validation of linearity or reportable range Samples 1, 2, 3, 4, 5, and 6 were analyzed in triplicate in a single run in random order. Theoretical (X)Mean (Y)Bias (%) Sample (N/A) Sample (-2.0) Sample (-0.7) Sample (-0.77) Sample (-1.24) Sample (-2.1)

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42 An example evaluation study: Cholesterol in serum Step 9: Estimation of SE from the linearity study which is a comparison of the method against reference method. The following statistics were obtained by linear regression analysis: Y = X mmol/L S Y,X = Mean X = 6.235Mean Y = Bias = | mean Y – mean X| = mmol/L This is the estimate of SE at the mean of the data.

43 An example evaluation study: Cholesterol in serum Step 9: Point estimate of SE at medical decision levels (X C ). For X C = 4.5 mmol/L, Y C = mmol/L SE 1 = | Y C – X C | = mmol/L Because SE 1 < TE A = 0.45 mmol/L, SE 1 is acceptable. For X C = 6.0 mmol/L, Y C = mmol/L SE 2 = | Y C – X C | = mmol/L Because SE 2 < TE A = 0.6 mmol/L, SE 2 is acceptable

44 An example evaluation study: Cholesterol in serum Step 10: Point estimate of TE Criteria for acceptable performance: TE A > TE = 3 x sd + | Y C – X C | For X C1 = 4.5 mmol/L, Y C1 = mmol/L and sd = TE 1 = 3 x = mmol/L < 0.45 mmol/L Performance acceptable For X C2 = 6.0 mmol/L, Y C2 = mmol/L and sd = TE 2 = 3 x = mmol/L < 0.6 mmol/L Performance acceptable

45 An example evaluation study: Cholesterol in serum Step 11: Medical decision chart X C1 X C2 Level mmol/L TE A mmol/L SE mmol/L RE mmol/L RE as % of TE A SE as % of TE A

46 Medical decision Chart X C1 X C2

47 Use of method decision chart. A method with: 1.Unacceptable performance does not meet the requirement for quality, even when the method is working properly. Not acceptable for routine operation. 2. Marginal performance provides the desired quality when everything is working correctly. But, difficult to manage in routine operation, requires total QC strategy, well-trained operators, aggressive preventive maintenance, etc. 3. Good performance meets requirement for quality and can be well- managed in routine service. Requires multirule procedure with 4-6 control measurements per run. 4. Six sigma or excellent performance is clearly acceptable and easy to manage in routine service and can be controlled

48 A comparison of methods experiment is performed to estimate inaccuracy or systematic error. This performed by analyzing patient samples by the new method (test method) and a comparative method, then estimate the systematic errors (SE) on the basis of differences observed between the methods. The systematic differences at the critical medical decision concentrations are the errors of interest. When possible, a “reference method” should be chosen for the comparative method. Any differences between a test method and a reference method are assigned to the test method.

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51 Interpretation of comparison of methods study. The differences are relatively small, not more than 2.2% across the concentration range of 2.0 – 15.0 mmol/L. The two methods have the same relative accuracy. The can be substituted for the other.

52 Recommended Minimum Studies for comparison of methods experiment. 1.Select 40 patient specimens to cover the full working range of the method. 2.Analyze 8 specimens a day within 2 hours by the test and comparative methods. 3.Graph results immediately on a difference plot and inspect for discrepancies. 4.Reanalyze specimens that give discrepant results. 5.Continue the experiment for 5 days if no discrepant results are observed.

53 Recommended Minimum Studies for comparison of methods experiment. 6. Continue for another 5 days if discrepancies are observed during the first 5 days. 7. Prepare a comparison plot of all the data to assess the range, outliers, and linearity. 8. Calculate the correlation coefficient and if 0.99 or greater, calculate simple linear regression statistics and estimate the systematic error at medical decision concentrations. 9. Use the medical decision chart to combine the estimates of SE and RE and make judgment on the total error observed for the method.

54 NATURE, 18 September 2003 Monkeys reject unequal pay. - Sarah Brosnan and Frans de Waal Working for peanuts. - Paul Smaglik


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