1. INTERNAL QUALITY CONTROL

2. Introduction
QC monitors activities related to the examination (analytic) phase of testing.
The goal of QC is to detect, evaluate, and correct errors due to test system failure, environmental conditions or operator performance, before patient results are reported.
When a diagnostic test is performed in the medical laboratory, the outcome of the test is a result.
The result may be a patient result or it may be a quality control (QC) result.

3. Introduction
Internal quality control involves the analysis of control samples with patient specimens, then evaluating the results 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 and patient testing can begin.
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.

4. Introduction
Let’s assume the measured value of potassium in a patient’s serum is 2.8 mmol/L (abnormally low).
But how does the person performing the test know that this result is truly reliable?
It could be possible that the instrument is out of calibration and the patient’s true potassium value is 4.2 mmol/L – a normal result.
The question of reliability for most testing can be resolved by regular use of quality control materials and statistical process control.
normal range for a potassium level is about 3.5 – 5.0 mmol/L

6. Definition Internal quality control (IQC) is:
“A set of procedures undertaken by laboratory staff for the continuous monitoring of operations and the results of measurements in order to decide whether results are reliable enough to be released”.
“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”.

7. Control Solutions, Control Materials
The International Federation of Clinical Chemistry defines a control solution or control material as:
“A specimen or solution which is analyzed solely for quality control purposes, not for calibration". 
We use the term control material or control product to refer to a control solution that is available, usually commercially, in liquid, frozen, or lyophylized form, packaged in small bottles suitable for use on a daily basis. 

8. Control Solutions, Control Materials
They can be obtained from manufacturers who specialize in the production of control materials, and also are often provided by the same companies who sell the reagents, kits, and instrument systems. 
In general, materials prepared from human sources have been preferred in the past, however because of the potential biohazard risk today, bovine based control products have become more popular.

9. Frequency of QC Analysis
The level of QC applied in the laboratory varies according to the number of analytical runs and the specimens analyzed per day.
The following protocol may be adopted by the laboratories according to the total number of specimens analyzed per analyte:
Less than 50 per day - apply at least one level QC once a day.
Between 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.

10. Characteristics of A Good Control
The composition of the control material should be as similar to the patient sample as possible, reacting in the same manner
The analyte concentration should be at medically significant levels
The constituents should be stable under storage for a long period of time prior to preparation
Material should have low vial-to-vial variability
The material should be ready to use or require a minimum of preparation and be readily available for emergency use
After a vial has been opened and the material prepared, it should remain stable for the period of use
The material should be available in large quantities
The material should be reasonably priced (but cost should not be the primary factor)
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11. Characteristics of A Good Control Control Matrix
Matrix refers to the substance or base from which the control material is prepared in addition to all the additives such as spiking materials, preservatives, etc. added to make the product desirable to the user.
Ideally, control materials should have the same matrix as the specimens being tested so that they will behave the same as the real specimen. For example, choose a:
whole blood control for point-of-care blood glucose analyzers and for blood gas and whole blood electrolyte analyzers to maintain a similar matrix;
use a serum/protein based control for analyzers that perform tests on serum or plasma. 

12. Characteristics of A Good Control Control Stability
When possible, it is desirable to purchase at least a one year supply of the same lot or batch number.
Many products are now available with expiration dates of more than two years.
The desired expiration date of the control product should be included in the specifications listed at the time of  purchase.

13. Characteristics of A Good Control Vial to vial variability
Commercial control materials that have been lyophilized or freeze dried must be reconstituted with water or specialized diluent, therefore, it is very important to standardize the reconstitution step. 
Use Class A volumetric pipettes, deionized Type 1 water, and instructions that specify the mixing time and the reconstitution time to minimize the vial to vial variability due to the preparation process.
Many liquid control products which eliminate the reconstitution process are now available.  These products are generally more expensive and sometimes contain additives or preservatives which could introduce sources of error due to matrix problems with certain methods. 

14. Characteristics of A Good Control Analyte levels
Constituent levels of quality control materials should be chosen at medical decision concentrations and/or at critical method performance limits such as upper and lower linearity limits. 
Two or three  different concentrations are often needed for each analyte. 
Choosing control materials at critical concentrations (medical and/or performance) will allow the analyst to estimate the random error at critical levels of the method during stable operation and to provide  a monitor at the most important performance levels for that analyte. 

15. Quantitative Procedures
In quantitative procedures, commercially prepared quality control sera are used with patient samples to detect systematic analytical errors and monitor precision
Prepare and test the material daily for a minimum of 20 consecutive working days, paying careful attention to instrument function
At the end of 20- day period all of the data is collected to calculate a mean, standard deviation, and coefficient of variation excluding the data known to be the result of mistakes and explained errors
coefficient of variation: It is defined as the ratio of the standard deviation to the mean .

16. Interpretation
Interpretation of the control result can take one of several forms:
Graphical interpretation using levey-Jennings or shewhart charts
Statistical and graphical interpretation by:
multi-rules,
cumulative summaries

17. Shewhart or Levey- Jennings control charts
In a stable test environment the distribution of the results of the same sample analyzed a number of times is a Gaussian distribution
Deviation indicates a systematic error
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

18. Shewhart or Levey- Jennings control charts

19. Shewhart or Levey- Jennings control charts
In a random distribution and in a correctly operating test system approximately:
65% 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
This means that one data point in 20 should be placed between either of the 2s and 3s limits
It would be very unexpected (0.3% chance) to observe a control value greater than 3 SD from the mean.

20. Shewhart or Levey- Jennings control charts
Run Number/ Date

21. Inspecting the pattern of plotted points can lead to the detection of increased random error and shifts and/or trends in calibration or in the analysis process

22. A shift is a sudden change in the mean value of the accumulated quality control values.
Precision is not affected but the plotted points stay consistently to one side or the other of the calculated mean value, indicating a shift in the distribution of control values with a new mean.

23. A trend is a continuous movement of values in one direction over six or more analytical runs.
Trends can start on one side of the mean and move across it or can occur entirely on one side of the mean.
Occurrence of shifts and trends is the result of either proportional or constant systematic error

24. Levy-Jennings charts can also demonstrate loss of precision by an increase in the dispersion of points on the control chart.
Values can remain within the +/-2 s and 3 s limits, but be unevenly distributed outside of the +/-1 s limits.
Random error is present if more than 1 in 20 values fall beyond the +/-2 s limits.

25. Westgard Multi-rules
By running and evaluating the results of 2 controls together, trends and shifts can be detected much earlier.
Westgard and associates have formulated a series of multi-rules to evaluate paired control runs known as Westgard rules.

26. Westgard Multi-rules 12S rule 13S rule 22S rule R4S rule 41S rule
10X rule
8-5 26

27. Westgard Multi-rules, 12S Rule
One of two control results falls outside ±2s
Warning Rule – does not cause rejection of a run
Alerts technologist to possible method or instrument malfunction

28. Westgard Multi-rules, 13s 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

29. Westgard Multi-rules, 22S Rule
22s - reject when 2 consecutive control measurements exceed the same mean plus 2s or the same mean minus 2s control limit

30. Westgard Multi-rules, 22S Rule
Both control results exceed their respective +2s limits, therefore there is a 22s 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.

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

32. Westgard Multi-rules, 41S Rule
The control charts for the high and low materials show that the last four control observations have exeeded their respective +1s limits, therefore a 41s rule violation appears to have occured across materials and across runs. 

33. Westgard Multi-rules, R4S Rule
R4s- reject when 1 control measurement in a group exceeds the mean plus 2s and another exceeds the mean minus 2s

34. Westgard Multi-rules, R4S Rule
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 R4s rule violation has occurred.
This most likely indicates a random error.

35. Westgard Multi-rules, 10x Rule
10x- reject when 10 consecutive control measurements fall on one side of the mean

36. Westgard Multi-rules, 10x Rule
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 10x rule violation across runs and across materials, which indicates that a systematic error most likely has occurred.

38. Rule Interpretation When controls fall within 2 sd’s, accept the run.
“Warning Rule” - One control ± 2 sd limit, hold patient results while inspecting control data with the 13s , 22s , R4s , 41s , and 10x rules.
If any of these additional rules indicates that the run is “out of control”, reject the run.
When a run is “out of control” determine the type of error occurring based on the control rule violated.
Look for sources of that type of error.
Correct the problem and reanalyze the whole run including controls.

39. What do the Control Rules Detect?
Type of Error
12S
Warning
13S
Random
22S
Systematic
R4S
41S
10x

40. Can you use the control rules "across runs" when the previous run has been rejected?
No, whenever you reject a run and correct a problem, you have to start over and collect the necessary number of control measurements to assess control status of the corrected process.
You can't use earlier measurements that were collected prior to correcting the problem because they no longer represent the current state of performance for the process.
Therefore, after correcting a problem, it is often useful to load up on controls to have enough information about the new state of operation.

41. 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

42. What are some causes? Quality Control Issue Possible 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

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

44. Systematic Corrective Actions
Check expiration date of the control.
Check expiration date of the reagent.
If a new control was used, make sure it was reconstituted properly.
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.
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.

45. Systematic Corrective Actions
If the new control value is out of control, troubleshoot the instrument (check sampling, reagent delivery, mixing, lamp integrity, and reaction temperature).
Recalibrate the method, especially if two or more controls have shifted.
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.
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.

46. 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 reported 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.

47. 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.

48. 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.