1. Westgard rules Dr Ahmed Aboamer MD in Clinical pathology

2. Definition  Internal quality control (IQC) 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 to make a diagnostic or therapeutic decision”.

3. INTERNAL QUALITY CONTROL  QC monitors activities related to the analytic phase of testing.  The goal of QC is to detect, evaluate, and correct errors due to test system failure, before patient results are reported.  Results of quality control evaluated statistically to determine the acceptability of the analytical run.  They are used to analyze the accuracy and precision performance of the assay or analyzer.  If the control is in range, it is assumed that the reagents and analyzer are performing correctly  When control samples do not produce accurate and precise results, it can be assumed that any patient results obtained at the same time are also erroneous. 3

4. Control Materials  Specimens that are analyzed or QC purposes are known as “control materials".  Characteristics of A Good Control composition of the control material should be as similar to the patient sample as possible Should be available in a stable form, in aliquots or vials should be ready to use or require minimum preparation Material should have low vial-to-vial variability from human sources however because of the potential biohazard risk, animal based control products have become more popular lyophilized, freeze-dried, or Liquid control material –materials with matrices representing urine, spinal fluid, or whole blood.

5. Control Materials  Concentration of analyte should be within healthy and abnormal reference intervals, ( medical decision concentrations)  Control materials can be purchased as assayed or unassayed  Frequency of QC Analysis: varies according to the number of analytical runs and the specimens analyzed per day. –Less than 50 per day - apply at least one level QC once a day. –Between 50-100 per day - apply two level QCs at least once a day. –More than 100 per day - apply two level QCs at least twice a day for such analytes.

6. Control Charts  Control charts are simple graphical displays in which the observed values are plotted versus the time  Lines or upper and lower control limits  When plotted points all within control limits, this occurrence generally is interpreted to mean that the method is performing properly; points falling outside control limits are problematic.  Control limits usually are calculated rom the mean (x) and the SD

7. Shewhart or Levey-Jennings control charts  The levey-Jennings control chart is derived from the Gaussian distribution indicating the mean and the one, two, and three standard deviation ranges on both sides of the mean  The chart illustrates the relationship between the levey-Jennings control chart and the Gaussian distribution from which it is derived  The figure also shows the expected percentage of results that should fall within each of the standard deviation ranges

8.  In a random distribution and in a correctly operating test system approximately:  68% of the values will be between the ± 1s ranges and will be evenly distributed on either side of the mean  95% of the values should fall between the ± 2s ranges  and 99.7% between the ± 3s limits  It would be very unexpected (0.3% chance) to observe a control value greater than 3 SD from the mean. Shewhart or Levey-Jennings control charts

9. Run Number/ Date

10.  Interpretation o control data is guided by certain decision criteria or control rules, which define when an analytical run is judged “in control” (acceptable) or “out o control” (unacceptable).  The control rules are given symbols such as A L, or n L, where A is the abbreviation or a statistic, n is the number of control observations, and L refers to the control limits.  For example, 1 3s refers to a control rule in which 1 observation exceeding the mean ± 3 sd  Similarly, 1 2s refers to a control rule in which 1 observation exceeds the mean ± 2 sd

11. Westgard Multi-rules  Two levels of control at different concentrations will be more efficient in monitoring the method when evaluated statistically  By running and evaluating the results of two controls together, trends and shifts can be detected much earlier  “ Multirule QC uses a combination of decision criteria, or control rules, to decide whether an analytical run is in-control or out-of-control. 11

12. Westgard Multi-rules  1 2S rule  1 3S rule  2 2S rule  R 4S rule  4 1S rule  10 X rule 12

13.  One of two control results falls outside ±2s  Warning Rule – does not cause rejection of a run  In the original Westgard multirule QC procedure, this rule is used as a warning rule to trigger careful inspection of the control data by the following rejection rules 13 Westgard Multi-rules: 1 2S

14. Westgard Multi-rules, 1 3S Rule  When the control limits are set as the mean ± 3s  A run is rejected when a single control measurement exceeds the mean plus 3s or the mean minus 3s control limit 14

15. Westgard Multi-rules, 2 2S Rule  2 2s - reject when 2 consecutive control measurements exceed the same mean plus 2s or the same mean minus 2s control limit 15 Level 1 Level 2

16. Westgard Multi-rules, R 4S Rule  R 4s - reject when 1 control measurement in a group exceeds the mean plus 2s and another exceeds the mean minus 2s 16 Level 1 Level 2

17. Westgard Multi-rules, 4 1S Rule  41s- reject when 4 consecutive control measurements exceed the same mean plus 1s or the same mean minus 1s control limit 17 Level 1 Level 2

18. Westgard Multi-rules, 10 X Rule  10x- reject when 10 consecutive control measurements fall on one side of the mean 18 Level 1 Level 2

19. 8 x - reject when 8 consecutive control measurements fall on one side of the mean. 12 x - reject when 12 consecutive control measurements fall on one side of the mean.

20. 6 x - reject when 6 consecutive control measurements fall on one side of the mean. 9x - reject when 9 consecutive control measurements fall on one side of the mean.

21. 2of3 2s - reject when 2 out of 3 control measurements exceed the same mean plus 2s or mean minus 2s control limit; In situations where 3 different control materials are being analyzed 3 1s - reject when 3 consecutive control measurements exceed the same mean plus 1s or mean minus 1s control limit.

22. 7 T - reject when seven control measurements trend in the same direction, i.e., get progressively higher or progressively lower. A related control rule that is sometimes used, particularly in Europe, looks for a "trend" where several control measurements in a row are increasing or decreasing: Chemometrics

24.  The advantages of multirule QC procedures are that false rejections can be kept low while at the same time maintaining high error detection.  This is done by selecting individual rules that have very low levels of false rejection, then building up the error detection by using these rules together.  False alarms are minimized by using the 1 2S rule as a warning rule, then confirming any problems by application of more specific rules that have a low probability of false rejection (serial testing).  True alarms or error detection are maximized by selecting a combination of the rules most sensitive to detection of random and systematic errors, then rejecting a run if any one of these rules is violated (parallel testing).

25. The key in how to apply control rules with multiple materials and multiple runs is to identify which control results represent consecutive measurements; e.g., if one measurement is made on each of two different control materials in an analytical run, control rules can be applied as follows: The two control results "within a run" can be inspected by applying a 1 3s rule to each material, as well as the 2 2s and R 4s rules "across materials." The 2 2s rule can also be applied to the last two measurements "within a material and across runs." The 4 1s rule can be applied to the two control measurements in the current run and the two measurements in the previous run, i.e., the rule can be applied "across materials and across runs". The 4 1s rule can also be applied to the last four measurements "within a material and across runs," which now requires the control results from the three previous runs. The 10 x rule can be applied to both control measurement in a run for the last five runs, or to the measurements on just one material for the last ten runs.

26. Because of these many possible applications of individual rules in a multirule QC procedure, it is best to provide specific directions for when to analyze controls, how to interpret the results, and what to do based on those results.  Statistical QC procedure. Use a 1 2s warning rule and the 1 3s /2 2s /R 4s /4 1s /10 x rejection rules with 2 control measurements per run.  Analysis of control materials. Analyze one sample of the Level A control and one sample of the Level B control in each run.  Interpretation of warning rule. If both control results are within 2s limits, report the patient test results. If one control result exceeds a 2s limit, inspect the control data as follows and reject the run if any control rule is violated

27.  Within run inspection of control results. Inspect the control results in the current run by applying the 1 3s rule to the results from each material and the 2 2s and R 4s rules across materials.  Across run inspection of control results.  Apply the 2 2s rule within each material across the last two runs;  apply the 4 1s rule within each material across the last 4 runs;  apply the 4 1s rule across the last two runs and the two measurements on each material;  apply the 10 x rule across the last five runs and the two measurements on each material.  Interpretation of rejection rules.  If none of the rules are violated, accept the run and report patient results.  If any one of the rules is violated, the run is out-of-control; do not report patient test results.

28. Determine the type of error Control RuleType of Error 1 2S Warning 1 3S Random 2 2S Systematic R 4S Random 4 1S Systematic 10xSystematic

29. What Do You Do Now?  Change Old Bad Habits - Recognize Problems:  Bad Habit #1: Repeat the control  Bad Habit #2: Try a new control  Develop Good Habits - Solve Problems:  Good Habit #1: Inspect control charts or rules violated to determine type of error  Good Habit #2: Relate type of error to possible causes  Good Habit #3: Relate causes to recent changes  Good Habit #4: Verify the solution and document the remedy

30. What are some causes? Quality Control IssuePossible Causes Values shifting within range (Systematic) Inadequate mixing of controls Controls left at suboptimal temperature for too long Variation between controls (ranges) Lot number change Values shifting out of range (Systematic) Any of the reasons described above Improper reconstitution of controls Error in control concentration Reagent contamination Deterioration of controls Instrument problem Trend (Systematic) Instrument change: o Reaction temperature o Sampling problem o Reagent delivery problem o Detector Problem Imprecision (Random) Improper mixing of reactions constituents Contamination during testing Pipetting variation Electrical Supply

31. Corrective Actions Things that can go Wrong Corrective Action Instrument malfunctionIdentify malfunction and fix Reagents: preparation, contamination, volume New reagents Tech errorIdentify error and repeat test Control specimen is old or prepared improperly Use new control

32. Systemic error  Systemic error is evidenced by a change in the mean of the control values.  The change in the mean may be gradual and demonstrated as a trend in control values or it may be abrupt and demonstrated as a shift in control values. Trend A trend indicates a gradual loss of reliability in the test system Shift Shift in QC data represent a sudden and dramatic +VE or –VE change in test system performance.

33. Systematic Corrective Actions 1.Check expiration date of the control. 2.Check expiration date of the reagent. 3.If a new control was used, make sure it was reconstituted properly. 4.Retest the control. If the new value is within acceptable limits, record both values and proceed with patient testing. The problem with the first value was probably random error, which is expected in one of every 20 values. 5.If the repeat value is still out of range, run a new vial of control. If the new control value is within acceptable limits, record the values and proceed with patient testing. The problem with the first set of controls was probably specimen deterioration.

34. Systematic Corrective Actions 6.If the new control value is out of control, troubleshoot the instrument (check sampling, reagent delivery, mixing, lamp integrity, and reaction temperature). 7.Recalibrate the method, especially if two or more controls have shifted. 8.If controls shift after a new reagent lot number has been introduced, rerun some normal and abnormal patient samples. If patient correlations are good, control shifts are probably acceptable. If they are poor, reagent may be bad. 9.Try a new lot number of reagent. If the problem is corrected, check with the manufacturer to find out if anyone else has reported problems.

35. How to deal with previous results?  When an analytical run is rejected because quality control is out of acceptable limits, it is often necessary to determine if patient results between the last acceptable run and the rejected run need to be repeated.  This decision should be based on the nature and size of the error.  A 5% bias has no clinical significance for most patients, but a 25% bias is unacceptable for nearly all patients.  Biases in between these extremes need to be examined on a case by case basis.  If analytical errors have clinical significance, then some of the patient specimens should be re-tested, starting with the samples analyzed just before the rejected control samples.

36. How to deal with previous results?  For example, suppose all glucose results in a run had a 10% negative bias.  A patient with a true blood glucose of 82 mg/dL would have been reported as 74 mg/dL.  Both values are normal and the results do not need to be corrected.  However, an error of this magnitude would be significant for patient samples near medical decision points such as values below 60 mg/dL, fasting glucose values near 140 mg/dL, and glucose tolerance tests near 180 mg/dL.

37. How to deal with previous results?  An acceptable strategy would be to repeat patient samples that were less than 60 or greater than 140 mg/dL, starting with samples analyzed just before the quality control failure.  Repeat testing should be done in reverse chronological order until the new glucose results closely match the original results.

38. Quantitative QC-Module 7 38

39. Quantitative QC-Module 7 39 Answers for activity  Day 21, 22, 24, 26, 27, 30, 31, 33, 34, 36-44 – in control  Day 23, 28, 29 – 1 2s  Day 25 - 1 3s  Day 32 – 2 2s  Day 35 - R 4s

40. http://www.westgard.com/lesson18.htm#terminology Example control results for this multiple rule application High: mean=250 and s=5) Low:mean=200 and s=4 Identify the rule at: 3,4,7,9,10, 11,12,14,20 Identify the rule at: 3,4,7,9,10, 11,12,14,20

41. Run 3 Both control results exceed their respective +2s limits, therefore there is a 2 2s rule violation across materials. A systematic error is most likely occurring and is affecting the results throughout the critical analytical range from at least 200 to 250 mg/dL. Chemometrics

42. Run 4 The high control result is below its -2s limit, which is a warning of a possible problem. Inspection with the 1 3s, 2 2s, and R 4s rejection rules that can be applied within the run do not confirm a problem. Note that the across-runs rules would not be applied because the previous run was rejected. Chemometrics

43. Run 7 The high control result exceeds its +3s limit, therefore there is a 1 3s control rule violation. This most likely indicates random error. Chemometrics

44. Run 9 The high control result is below its -2s limit. Inspection of the control results by the rejection rules does not confirm a problem. Chemometrics

45. Run 10 The control chart for the high control material shows that the last two measurements have both exceeded the -2s limit, therefore a 2 2s rule violation has occurred within material and across runs. This situation would be consistent with a loss of linearity that is beginning to affect the high end of the analytical range. Chemometrics

46. Run 11 There is a 1 2s warning on the high level control material, but inspection doesn't show any other rule violations, therefore, the patient test results in this run can be reported. Chemometrics

47. Run 12 The control charts for the high and low materials show that the last four control observations have exeeded their respective +1s limits, therefore a 4 1s rule violation appears to have occured across materials and across runs. Chemometrics

48. Run 12 Note, however, that the QC protocol specified that a control result had to first exceed a 2s control limit before initiating the application of the 4 1s rule. Therefore, according to the protocol, this run would not be interpreted as out-of- control. Chemometrics

49. Run 14 The control results for the high material exceeds its +2s limit and the control result for the low material exceeds its -2s limit, therefore an R 4s rule violation has occurred. This most likely indicates a random error. Chemometrics

50. Run 14 The control results for the high material exceeds its +2s limit and the control result for the low material exceeds its -2s limit, therefore an R 4s rule violation has occurred. This most likely indicates a random error. Chemometrics

51. Run 20 The last five control results on the high material and the last five results on the low material all are lower than their respective means, giving a total of ten consecutive control results on one side of the mean. There is a 10 x rule violation across runs and across materials, which indicates that a systematic error most likely has occurred. Chemometrics

52. Exercise http://www.westgard.com/lesson12.htm QC - THE LEVEY-JENNINGS CONTROL CHART Answer http://www.westgard.com/lssn12p2.htm

53. LOGO