1. Control of Analytical Variables Dr. Roula Hamid MSc Clin Biochem Central Puplic Health Laboratory QC Chemistry

2. Today is not the golden age of quality in healthcare laboratories. We can & should be doing the better. James O Westgard 2003

3. Introduction “Nice to Know”

4. Quality Planning (QP( Quality Laboratory Process (QLP( Quality Control (QC( Quality Assessment (QA( Quality Improvement (QI( Goals, Objectives, Quality Riquirements Total Quality Management framework for management of quality in healthcare laboratories

5. The “five-Q” framework defines how quality is managed objectively with the “scientific method” or the PDCA cycle PDCA Plan, do, check & act QP (PLANING steps) QLP (standard process for the way things are DONE) QC & QA (CHECK) QI (mechanism through which to ACT on those measures)

6. Control of Analytical Variables

7. Analytical Variables must be controlled carefully to ensure accurate measurements by analytical methods

8.  Documentation of analytical protocols  Monitoring technical competency  Statistical control of analytical methods  EQA  New quality initiatives

9. Documentation of Analytical Protocols  CLSI defines a process as a set of interrelated or interacting activities that transform input into output.

10. CLSI document describes the following section to be included in a laboratory procedure : A.Title: clear & concise B.Purpose or principle: e.g. this process describes how …., C.Procedure instructions: how to do D.Related documents: listing of other procedure E.References: source of information F.Appendixes or attachments G.Auther(s): author(s) of document H.Approved signatures

11. Monitoring Technical Competency  Proper training of laboratory personnel to establish uniformity in technique is important.

12. Statistical control of analytical methods  Control materials  General principles of control chart  Performance characteristics of a control procedures  Westgard multirule chart  Identifying sources of analytical errors

13. Control materials  Specimens that are analyzed for QC purposes are known as control materials  They need to be available 1)In a stable form 2)In vial or aliquots 3)& for analysis over an extended period of time

14. General principles of control chart  A common method used to compare the values observed for control materials with their known values is the use of control charts

15. Figure: Gaussian frequency distribution

16. Control limit a) Stable performance b) Accuracy problem; shift in mean c) Precision problem; increase in standard deviation Figure: Conceptual basis of control charts. Frequency distributions of control observations for different error conditions

18. Performance characteristics of a control procedures The knowledge of performance characteristics of control procedures is necessory to select the control rules that detect relevent laboratory problems without causing too many false alarms

19. Westgard multirule chart  The probability of false rejection is kept low through selection of only those rules with low  The probabilities for error detection is improved through selection of those rules that are particularly sensitive to random & systemic errors.  The use of multirule procedure is similar to the use of a Levey-Jennings chart, but the data interpretation is more structured.

20. Figure: Decision path for QC program

21. To use the multirule procedure, the following steps are used : C- 2 control samples are introduced into each analytical run B- computer software; control values on y-axis ±4s, horizontal lines for 1s,2s & 3s, & x-axis for days A- 20 days, 2 different materials, mean & SD are calculated for both.

22. D- Analytical run is accept & patient results reported Control observations fall within 2s limits

23. The analytical run is rejected & the patients results are not reported If any is out Additional rules are applied e.g. 13s.2 2s,R 4s & 10x Patient results are held One of control observations exceed 2s limits

24. E- The analysis of the entire run repeated including both control & patient samples The problem is corrected Looking for the source of that error The type of error is determined based on the control rule that have been violated

25. Figure : Westgard multirule chart with control limit drawn at the mean ± 1s, 2s & 3s. Chart for high concentration

26. Figure : Westgard multirule chart with control limit drawn at the mean ± 1s, 2s & 3s. Chart for low concentration

27. Identifying sources of analytical errors ERRORS SystemicRandom Inspection OR Checklist Analytical methods EquipmentsReagentsSpecimens Alerted to a control problem

28. Systemic Errors Impure calibration materials Improper preparation of calibrating solution Erroneous set points & assigned values Unstable calibrating solutions Contaminated solutions Inadequate calibration technique Nonlinear or unstable calibration function Inadequate sample blankUnstable reagent blanksRandom Errors Lack of reproducibility in the pipetting of samples & reagents Dissolving of reagents tablets & mixing of sample & reagents Lack of stability of temperature baths, timing regulation, & photometric & other sensors

29. EQA Procedures used to compare the performance of different laboratories (EQA)

30. IQC & EQA are complementary IQC For daily monitoring of accuracy For daily monitoring of precision EQA Maintenance of long term accuracy of the analytical methods

31. Features of External Quality Assessment Programs  EQA program available to the clinical laboratories by professional societies & manufactures of control materials  All the participating laboratories analyzing the same lot of control material  Results are tabulated periodically & sent to the sponsering group for data analysis  The reports often includes extensive data analysis, statesical sumaries & plots

32.  The mean of values of all laboratories is taken as the true or correct value & is used for comparision with the indivisual laboratory’s mean  Different approaches for data anaalysis e.g. t- test, SDI,Youden plots & Levey-Jennings plots

33. New quality initiatives The six sigma process Lean production ISO 9000

34. The six sigma process  The six sigma control is an evolution in quality management  6 sigma or 6 SD of process variation should fit within the tolerance limits for the process

35. -6s -5s -4s -3s -2s -1s 0s 1s 2s 3s 4s 5s 6s + Tolerance specification - Tolerance specification Target Figure : Six sigma goal for process performance “ tolerance specification” represents the quality requirements

36. Lean production  It is a quality process that is focused on creating more value by eliminating activities that are considered waste  e.g. Lean team at Saint Mary’s Hospital used lean production to improve the efficiency of its paper ordering system for lab work in their ICU.

37. Six sigma process Improve quality Management of health care facilities & clinical laboratories Lean Production Increase efficiency

38. ISO 9000  The International Standard Organization (ISO) has developed the ISO 9000 standards  It is a set of 4 standards (ISO 9001-9004) enacted to ensure quality management & QA.  ISO 9000 represents an international consequence on the essential features of a QS to ensure the effective operation of an organization

39. Joint Committee for traceability in Laboratory Medicine The traceability of values assigned to calibrators &/or control materials must be assured through available reference measurement procedures &/or available reference materials of a higher order

40. Definitive method Method validation Primary reference material Reference method Method validation, external quality control Field method Control material IQC True value Observed value traceability Figure: Structure of an accuracy based measurement system showing relationships among reference methods & materials Secondary reference method

41. References Burtis,C.A., Ashwood,E.R. & Br uns,D.E. Fundamentals of Clinical Chemistry. 2008..6 th.ed. SAUNDERS ELSEVIER. P:249-262. Arneson,W. & Brichell,J. Clinical Chemistry ‘A Laboratory Perspective’. 2007. F. A. Davis Company. P:53-72. Westgard,J.O. Internal Quality Control: Planning & Implementation Strategies. 2003. Ann Clin Biochem. 40; 593-611.

42. Thank you