BIOT6002 Lecture 11 Immunoassay QC PDF

Standardisation & Quality Assurance – Part 2 BIOT6002: Lecture 11 Lecturer: Caroline A Browne, Ph.D. Learning Objectives For Validation Reports, describe how to measure Precision Accuracy Define the principles of Internal Quality Control External Quality Assessments Explai...

Standardisation & Quality Assurance – Part 2 BIOT6002: Lecture 11 Lecturer: Caroline A Browne, Ph.D. Learning Objectives For Validation Reports, describe how to measure Precision Accuracy Define the principles of Internal Quality Control External Quality Assessments Explain how to use Control Charts Validation Report -Assessment of Precision Precision “The closeness of agreement between independent test results obtained under stipulated conditions” Measured as repeatability (r), intermediate precision (Rw), and reproducibility (R). Precision is hard to quantify so it is the inversely related imprecision that is commonly reported. The Coefficient of Variation (%CV) expresses as a % of the mean %CV = s / mean x 100 Validation Report -Assessment of Precision Precision data is required when selecting a method or instrument initially for routine use – Validation Precision of assay must be determined over time using standardised procedures – Internal Quality Control Assessment of Precision Repeatability (within-run or within-day) - variability observed when Laboratory, technician, days, instrument, reagent lot, are held constant Time between the measurements kept to a minimum. Reproducibility All factors varied. Measurements carried out over several days. Intermediate precision (between-run or between day) All factors except laboratory are allowed to vary and for clarity the factors changed should be stated in the validation report. Example Procedure for Validation 1. Collect samples with known high and low concentrations of the measurand. Pool samples if necessary. 2. Make 25 aliquots of each sample and store at −80°C pending analysis. 3. At day 1–5 measure 5 replicates of each sample. Note: the days need not to be consecutive, only different. 4. Insert data, separate days on different rows of an excel file. 5. Calculate the mean value, SD, %CV to assess repeatability and intermediate precision. Evaluation of precision of methods Measurement of intra-assay precision: Analyse ~ 20 replicate samples/standards in one batch or run & calculate the within assay %CV. This should be repeated with replicate specimens of different concentrations. Intra-assay precision: depends on the nature & concentration of samples and the performance of analysis at the time & cannot be used as a definitive measure of precision of method. Measurement of inter-assay/ inter-day precision: Select 3-5 samples / standards over the analytical range. One per day should be analysed for 20 consecutive days The inter-day %CV is calculated from 20 results Assessment of Accuracy Accuracy “the closeness of a measured value to a standard or known value” Calibration standards: All quantitative measurements require a standard (calibrant) A solution containing a known amount of component to be assayed. Primary standard: One in which the concentration is determined by dissolving a weighed amount of substance in a solvent and making up to a stated volume or weight Accuracy depends on the purity of standard material and quality of preparation Assessment of Accuracy Secondary standard: is one in which the concentration is determined by an analytical method of known reliability. Internal standard: is a substance not normally present in the sample & clearly distinguishable from the test analyte which is added in known amount to the sample. Assessment of Accuracy Analytical methods can be classified into 3 groups according to their accuracy: 1. Definitive method: is one with no known source of inaccuracy 2. Reference method: is one which after exhaustive comparisons with a definitive method has been shown to have negligible inaccuracy 3. Method of known bias: should be tested against a reference method. Methods with little or no bias are referred to as standard methods Assessment of Accuracy 1. Recovery experiments: depend on the determination of a substance in a pair of identical samples to one of which is added a known amount of the pure substance. The difference in the results is the amount recovered & is expressed as % of the amount added (% recovery) 2. Control Materials: Specimens with known values e.g. commercial control materials (certified reference materials) can be used for comparison. 3. Statistical Comparison of methods: the best method of assessing the accuracy of a method is to compare the results with a method of known accuracy ( e.g. t-test or ANOVA). Principles of IQC for immunoassay 1. IQC samples should be of appropriate: Type – identical to samples used routinely Concentration – appropriate testing range Volume & stability – sufficient to cover a reasonable period of time e.g. 6 months Commercially available IQC material: Expensive (unassayed material available for precision studies) Errors in reconstitution In house preparation of pools: Can be hazardous depending on source Addition of exogeneous analyte, or dilution to within range Principles of IQC for immunoassay 2. IQC procedures should provide reliable estimates of intra-batch & inter-batch errors. Precision: is defined as the agreement between replicate measurements (random error) Drift: refers to a progressive/non-random change in results due to e.g. Temperature variations across plate, unmixed reagents or inadequate incubation times Basic IQC statistics IQC statistics How they are measured Intra-batch precision Intra-sample, intra batch precision profile Intra-batch drift i. Mean difference between Repeat Analytical Controls (RAC) at the beginning & the end of the batch. ii. Standard curve, beginning & end of the batch Basic IQC statistics IQC statistics How they are measured Inter-batch precision i. Standarddeviation of RAC between batches ii. Standard deviation of IQC pools Inter-batch drift Current mean of results of IQC pool compared with previous mean. Precision Profile Plot Three important applications: i. Determination of the working range of the assay ii. Rejection of sample outliers in raw data iii. Shape of the plot may reveal unsatisfactory quality of the batch Presentation & Interpretation of IQC data Quality control charts or Shewhart charts provide: i. A visual display of information for day-to-day analysis ii. A means of forecasting trends over a longer period of time. A measured quantity or a statistic derived from it is plotted versus time Control Charts Any of the following statistics can be used to plot a control chart: i. The result obtained from a control sample or the % difference between the result and a mean or target value ii. The mean of duplicate analyses of a control sample iii. The standard deviation or %CV calculated for the batch, day, week or month. Control Charts Control limits are incorporated in the chart set at +/- 2SD and +/- 3SD. The position of the control limits are set according to the situation & may be more stringent than these limits Warning Limits: +/- 2SD: if value is outside these limits, then the method needs to be investigated for unstable reagents, temperature control etc Action Limits: +/- 3SD: if value is outside these limits, then all results are wrong & fault must be investigated, rectified & assay repeated Charts can be used to indicate precision & accuracy as well as changes in these. The most common is a chart of control samples which should show a sequence of points randomly distributed about the central mean line. Control Charts File:Control chart.jpg Non random patterns may be of several different types: 1. Extreme variations due to gross errors e.g. outliers due to mistakes of sample identification, calculation etc. – can be ignored and taken as gross errors rather than random analytical variation. 2. Jumps in control line indicates a sudden alteration in accuracy due to e.g. a change in standard or reagent. 3. A sequence of points may lie on one side of central line but are still within control limits – indicates inaccuracy. Control Charts – Common Mistakes Misinterpreting Random Variation: Treating random variation as a sign of a problem can lead to unnecessary adjustments. Ignoring Special Causes: Failing to investigate points outside control limits can overlook significant issues. Incorrect Control Limits: Using incorrect calculations for control limits can lead to misinterpretation. Overcomplicating the Chart: Adding too much data or unnecessary details can make the chart difficult to read. Lack of Training: Inadequate training on how to use and interpret control charts can lead to misinformed decisions. Operation of IQC programme IQC results evaluated by person other than the analyst Rejection of batches/results: Imprecision – technique & precision of instrumentation Inaccuracy – preparation & use of standards Cumulative IQC data reviewed to monitor long term trends in performance Comparison with External Quality Assessment Scheme (EQAS) Review meetings Principles of EQAS Scheme Design: Frequent sample distribution to provide relevant & statistically valid data Rapid return of EQA reports to participants Accurate documentation of methods used Common units for reporting results EQA samples assayed by participants in precisely the same way as routine specimens Principles of EQAS Sample Material: Should behave identically to real samples in all assay systems Analyte concentrations appropriate to use of test Stable under conditions of sample distribution Present no avoidable infectious hazard Principles of EQAS Definition of target values: Reference method values provided where possible Validate accuracy & stability of values Assessment of performance: Appropriate statistics used Overall, method group & individual laboratory performance estimated Other aspects of performance (e.g. Test interferences, interpretation), assessed where appropriate Other aspects of QA in immunoassays Methods, protocols & equipment: Protocol transfer from R&D assay to routine laboratory Equipment & laboratory facilities adequate e.g. Incubators, plate washers, pH meters, storage facilities Calibration of instruments: pipettes, dispensers, microtitre plate readers, automated plate washers. Learning Objectives For Validation Reports, describe how to measure Precision Accuracy Define the principles of Internal Quality Control External Quality Assessments Explain how to use Control Charts