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IQC best practice

IQC best practice. Annette Thomas Weqas Director Cardiff and Vale University Health Board Chair IFCC C-AQ. Quality Assurance of diagnostic testing. Users should have a sound understanding of the principles of quality assurance such as: Quality management system

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IQC best practice

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  1. IQC best practice Annette Thomas Weqas Director Cardiff and Vale University Health Board Chair IFCC C-AQ

  2. Quality Assurance of diagnostic testing Users should have a sound understanding of the principles of quality assurance such as: • Quality management system • Internal quality control (IQC) • External quality assessment (EQA) • Audit • Risk Management QA includes all the measures taken to ensure investigations are reliable and safe. These include: • Correct identification of the patient • Test selection • Obtaining and analysing a satisfactory specimen • Applying IQC and EQA • Recording results promptly and correctly • Interpreting results accurately • Taking appropriate action • Documenting all procedures

  3. Definitions . Quality Control (QC) refers to procedures for monitoring the work processes, detecting problems, and making corrections prior to delivery of products or services. Statistical process control, or statistical quality control, is the major procedure for monitoring the analytical performance of laboratory methods. Quality Assessment (QA) refers to the broader monitoring of other dimensions or characteristics of quality. Characteristics such as turnaround time, patient preparation, specimen acquisition, etc., are monitored through QA activities. Proficiency testing (EQA) provides an external measure of analytical performance (also some pre and post analytical). Quality Improvement (QI) is aimed at determining the causes or sources of problems identified by QC and QA. May require problem-solving tools (such as the flowchart, Pareto diagram, Ishikawa cause and effect diagram, force field analysis, etc)

  4. IQC vs EQA

  5. ISO 15189:2012 - what does it say? Design– 5.6.2. The laboratory shall design quality control procedures that verify the attainment of the internal quality of results. Special attention should be given to elimination of mistakes in the pre and post examination processes. Material– 5.6.2.1 QC shall react to the exam system in a manner as close to patient samples as possible. QC should be periodically examined along with patient samples, with a frequency that is based on the stability of the procedure. Note: The lab should choose conc. of QC especially at or near clinical decision values that ensure the validity of decisions made. Note: Use of 3rd part QC should be considered, either instead of, or in addition to any QC material supplied by the reagent or instrument manufacturer.

  6. ISO 15189:2012 - what does it say? QC Data –5.6.2.2 The lab shall have a procedure to minimise the risk of significantly different or aberrant patient examination results being reported in the event of QC rule failure . This is poor wording as for a method that is excellent (6S) two results could be statistically different but not clinically significant and a method that is poor (2S), two results could be statistically insignificant but clinically significant. Note: When QC rules are violated, examination results should normally be rejected and relevant patient samples re-examined after the error condition has been corrected and within specification performance is verified. The lab should also evaluate the results from patient samples that were examined after the last successful QC event. QC data shall be reviewed at regular intervals to detect trends in exam performance that may indicate problems in the exam system. When such trends are noted preventive actions shall be taken and recorded. Note: Established statistical techniques such as Shewhart/ Levey-Jennings charts and process control rules should be used wherever possible to continuously monitor examination system performance.

  7. Design of IQC

  8. Design of IQC

  9. Specification Hierarchy Data from outcome studies Improvements in methods / technology

  10. How to choose analytical specification

  11. Importance of Performance Specifications Sverre Sandberg, EQALM 2015

  12. Design of IQC

  13. Design of IQC

  14. TAE is defined as the percentage (usually 95%) of the analytical error for a measurement procedure. Example Protocol 125 patient samples assayed singly on candidate method and compared with comparative method assayed in in duplicate over 10 days (10-15 samples per day). Undertake non parametric analysis of the differences between the methods calculating the 2.5thcentile (low Limit) and 97.5thcentile (High limit). Compare with the ATE.

  15. How good is the method? • Can identify assays that require improvement • Can be used to determine optimal QC rules • Can provide guidance on the frequency of IQC Sigma metric = (TEa – bias (observed)/CV (observed) Simple measure of the quality of the assay can be obtained using Six sigma approach.

  16. How to Calculate Sigma Sigma metric = (TEa – Bias)/CV e.g., for testing process assume TEa = 10.0% Effect of bias • if CV = 2.0% and Bias = 0.0%, then Sigma = 5.0 [10.0-0)/2] • if CV = 2.0% and Bias = 1.0%, then Sigma = 4.5 [(10.0-1)/2] • if CV = 2.0% and Bias = 2.0%, then Sigma = 4.0 [10.0-2)/2]if CV = 2.0% and Bias = 3.0%, then Sigma = 3.5 [10.0-3)/2] Effect of Imprecision • if CV = 1.0% and Bias = 2.0%, then Sigma = 8.0 [10.0-2)/1]if CV = 1.5% and Bias = 2.0%, then Sigma = 5.3 [10.0-2)/1.5]If CV = 3.0% and Bias = 2.0%, then Sigma = 2.7 [10.0-2)/3] Use the calculated Sigma metric to determine the appropriate QC, with the aid of available QC planning tools.

  17. Calculating Sigma

  18. Design of IQC

  19. IQC Strategies • Statistical & Westgard Multi Rules. • Six Sigma: A six sigma process is one in which 99.99966% of all opportunities to produce some feature of a part are statistically expected to be free of defects (3.4 defective features per million opportunities). This QC application makes use of medical cutoffs as tolerance limits. Westgard Sigma Rules. • Moving Average: Using patient test results to assess the performance of a laboratory testing process and to identify changes in process stability. • Risk Based QC: Patient risk based assessment or a risk-based approach to monitoring lab testing • Individualized Quality Control Plans (IQCP): is the CLIA Quality Control (QC) procedure for an alternate QC option. It includes risk assessment, quality control plan, and quality assessment. It is likely IQCP will be a key focus for point-of-care testing (POCT).

  20. IQC Planning - Questions to consider • What is the risk of getting it wrong? • How complex is my device? What is the performance? Is it fit for purpose? • I have an electronic QC, do I still need to perform IQC ? • What frequency should I be setting for testing IQC? • What acceptance limits should I set? • What material is available – is it simple to use, commutable, is it near the cut off? • What is the storage and stability? • How do I analyse trends? • How do I deal with qualitative tests? • What procedure do I need the user to follow if the results are outside acceptable limits?

  21. CLSI EP23™—Laboratory Quality Control Based on Risk Management EP23 describes good laboratory practice for developing a QCP based on the manufacturer’s risk mitigation information, applicable regulatory and accreditation requirements, and the individual health care and laboratory setting. James H. Nichols, PhD, DABCC, FACB, Chairholder of the document development committee

  22. Identify candidate IQC strategies the control materials used, the number of control samples analyzed, the location of these control samples in an analytical run, the quality control rules

  23. The control material

  24. Statistical QC -Basic Principles

  25. Control charts Levey-Jennings: • Their design is based on the assumption that: • The values resulting from previous measurements are subject to random variation • This variation follows a uniform (normal) distribution • Extremely rare (0,3%) a value> mean ± 3s • Unlikely (5%) a value> mean ± 2s • Very likely (32%) a value> mean ± 1s (limits without a value

  26. + 2sd - 2sd Levey-Jennings • measure control material for 20 days using the method (assay) and the instrument (analyzer) to evaluate • from those values calculate the meanand the SD Mean Mean = 6 SD = 1.5

  27. Control Rules Rules to decide whether a series of measurements is under control or out of control Control rules control limits 12s mean ±2s 13s mean ±3s Single rule methods Multiple rules methods

  28. QC Data 12s Report Results 13s 22s R4s 41s 10x Take Corrective Action “Westgard rules” James O Westgard Internal quality control: planning and implementation strategies Ann Clin Biochem 2003; 40: 593–611

  29. Control rules Generally used for 2 or 4 controls per run.

  30. Also use 8 or 12

  31. For 3 controls

  32. Design of IQC

  33. QC Tools to determine appropriate QC rules The tools include power function graphs , critical-error graphs , QC Selection Grids, charts of operating specifications (OPSpecs chart), and the QC Validatorand EZ Rules 3 computer programs . • Simplest is to use: Westgard Sigma RulesTM

  34. Power function graphs • Pfrprobability of false rejection should be close to zero (max 5%, 1% desirable) • Pedprobability for error detection should be close to 1.00 (desirable 0.90 – 90% chance of detecting a critical systematic shift.) • Determine critical systematic shift, • Calculate Sigma metric

  35. Power function graphs Rule complexity 4σ a multirule with n=4 Multi rule 5σ 13s/22s/R4s with n=2 • 6σ 13s rule with n=2 1 3s 1 control www.westgard.com

  36. The dashed vertical lines that come up from the Sigma Scale show which rules should be applied based on the sigma quality determined in your laboratory. For example: 6-sigma quality requires only a single control rule, 13s, with 2 control measurements in each run one on each level of control). The notation N=2 R=1 indicates that 2 control measurements are needed in a single run.

  37. Selecting the Rule

  38. Frequency of IQC Risk based approach. Combining Sigma and risk i.e. No of samples processed, reagent stability, impact of incorrect result etc. Clin.Chem Lab Med 2011; 49:793-802

  39. Implementation strategies • Don’t use 2sd control limits – Pfr = 9% (n=2) • Don’t use the same control rules for all tests • Select IQC based on quality required for the test and the precision and accuracy of the method • Minimize false rejections in order to maximise response to real problems • Build in error detection necessary to detect medically important errors. • Complement IQC with other QA and QI.

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