1. MLAB 2401: Clinical Chemistry
Quality Control, Quality Assessment and Statistics

2. Quality Assurance/Assessment (QA)
An all inclusive / comprehensive system monitoring the accuracy of test results where all steps before, during and after the testing process are considered. Includes pre-analytic, analytic and post analytic factors
Essentials include commitment to quality, facilities, resources, competent staff, and reliable procedures, methods and instrumentation
Provides a structure for achieving lab and hospital quality goals

3. Quality Control (QC)
QC systems monitor the analytical process; detect and minimize errors during the analysis and prevent reporting of erroneous test results.
It uses statistical analysis of test system data
Requires following published rules
Westgard Rules

4. Types of QC Internal External Daily Establishment of reference ranges
Validation of a new reagent lot and/or shipment
Following instrument repair
Proficiency testing
Determination of laboratory testing performance by means of intralaboratory comparisons
CAP, CLIA, The Joint Commission requirement
Must be integrated within routine workload and analyzed by personnel who are running the tests.
Ongoing evaluation of results to correct for unacceptable results
Used to access employee competency

5. Pre-Analytical & Analytical Causes of Error

6. Post- Analytical Causes of Error
Incorrect reference values
Physician not notified of a panic or critical value
Incorrect interpretation of lab results by physician
Incorrect data entry of lab result

7. Introduction to Statistical Analysis
When evaluating laboratory results, how do we determine what is normal or acceptable? In other words: What is “normal” or “OK”?
When does a laboratory test result become “weird” or “abnormal” ? When do we become uncomfortable with a result?
At some point we have to draw a “line in the sand” … on this side of the line you’re normal … on the other side of the line you’re abnormal. Where and how do we “draw the line” ?
Answer: Statistics are used to determine the lines of ‘normal’ and ‘acceptable’.

8. Statistical Concepts
Statistics is a (science of )branch of mathematics that collects, analyzes, summarizes and presents information about “observations.”
In the clinical lab, these “observations” are usually numerical test results
A statistical analysis of lab test data can help us to define
Reference ranges for patient’s (normal and abnormal)
Acceptable ranges for control specimens ( “in” and “out” of control)

9. Measures of Central Tendency
Mean (x̄) - the mathematical average of a group of numbers, determined by adding a group of numbers (events) and dividing the result by the number of events
Median - determined as the ‘middle’ of a group of numbers that have been arranged in sequential order. That is to say, there are an equal number of numbers on either side of the ‘middle’ number. In an odd # of observations, it is the middle observation. In an even # of observations, average the two middle values.
Mode - the number that appears most frequently in a group of numbers. There can be more than mode, or none at all.

10. Gaussian/Normal Distribution
All values are symmetrically distributed around the mean
Characteristic “bell-shaped” curve
Assumed for all quality control statistics

12. Accuracy and Precision
The degree of fluctuation in the measurements is indicative of the precision of the assay.
Precision-refers to the ability to get the same (but not necessarily ‘true’) result time after time.
The closeness of measurements to the true value is indicative of the accuracy of the assay.
Accuracy - An accurate result is one that is the ‘true’ result.

13. Precise and Accurate

14. Precision and Accuracy
Precise and inaccurate
Imprecise and inaccurate
Random Error
Systematic Error

15. Systematic error
Systematic change in the test system resulting in a displacement of the mean from the original value
Systematic error of an analytic system is predictable and causes shifts or trends on control charts that are consistently low or high

16. Causes of Systematic Error
Change in reagent or calibrator lot numbers
Wrong calibrator values
Improperly prepared reagents
Deterioration of reagents or calibrators
Inappropriate storage of reagents or calibrators
Variation in sample or reagent volumes due to pipettor misalignments
Variation in temperature or reaction chambers
Deterioration of photometric light source
Variation in procedure between technologists

17. Random Error
Imprecision of the test system causing a scatter or spread of control values around the mean

18. Causes of Random Error Air bubbles in reagent
Improperly mixed reagents
Reagent lines, sampling, or reagent syringes
Improperly fitting pipette tips
Clogged or imprecise pipetter
Fluctuations in power supply

19. Bias
Bias – the amount by which an analysis varies from the correct result.
Example, If the Expected Value is 50 units, and the result of an analysis is 47, the bias is 3 units.

20. Statistical Formulas Standard Deviation (SD)
Is a mathematical expression of the dispersion of a group of data around a mean.

21. Standard Deviation :
n = the number of observations (how many numerical values )
Σ = the sum of … in this case, the sum of
= the mean value
X = the value of each individual observation
The Standard Deviation is an expression of dispersion … the greater the SD, the more spread out the observations are

22. Standard Deviation and Probability
For a set of data with a normal distribution, a value will fall within a range of:
+/- 1 SD 68.2 % of the time
+/- 2 SD 95.5% of the time
+/- 3 SD 99.7% of the time

23. Statistical Formulas Coefficient of Variation (CV)
Indicates what percentage of the mean is represented by the standard deviation
Reliable means for comparing the precision or SD at different units or concentration levels
Expressed as a percentage
CV% =
Standard deviation X 100
mean

24. Coefficient of Variation (CV) %
Analyte:
FSH Concentration SD CV 1
0.09
9.0 5
0.25
5.0 10
0.40
4.0 25
1.20
4.8
100
3.80
3.8
The smaller the CV, the more reproducible the results: more values are closer to the mean.
Useful in comparing 2 or more analytical methods
Ideally should be less than 5 %

25. Establishment of a QC System
Two or three levels of control material used
A control is a material or preparation used to monitor the stability of the test system within predetermined limits
Measure of precision and reproducibility
Purpose: verify the analytic measurement range of instrument for a specific analyte

26. Establishment of a QC System
Control material matrix should resemble actual specimens tested
Lyophilized/liquid
Assayed
Mean calculated by the manufacturer
Must verify in the laboratory
Unassayed
Less expensive
Must perform data analysis in house

27. Establishment of a QC system
Collecting data
Run assay on control sample & manually enter control results on chart
One chart for each analyte and for each level of control

28. Establishment of a QC system
Collecting data
Many modern chemistry analyzers have computer program that maintains the QC log.
i.e Dade Dimension

29. Collecting Data for QC Charting techniques
Levey Jennings chart is a graph that plots QC values in terms of how many standard deviations each value is from the mean

30. Use of Standard Deviation
Once you have determined the standard deviation, must use the information to evaluate current/ future analysis.
Most labs make use of ± 2 SD or 95% confidence limit. To put this into a workable form, you must establish the range of the ± 2 SDs

31. So, how do we determine the range of acceptable results ?
Scenario
Mean of group of control values = 104 mg/dL
Standard Deviation = ± 5 mg/dL
Determine the Range of ± 2SD; (which will allow you to evaluate acceptability of performance of the control on subsequent days.)
Is a control value of 100 mg/dL acceptable?

32. Shifts and Trends Shift
QC data results are distributed on one side of the mean for 6-7 consecutive days
Trend
Consistent increase or decrease of QC data points over a period of 6-7 days

33. But what if your control specimen is “out of control?”
“Out of control” means that there is too much dispersion in your result compared with the rest of the results
This suggests that something is wrong with the process that generated that observation
Patient test results cannot be reported to physicians when there is something wrong with the testing process that is generating inaccurate reports
Remember … No information is better than wrong information

34. Westgard System

35. But what if your control specimen is “out of control?”
Corrective methods
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

36. QC terms AMR= Analytical Measurement Range
Range of analyte values that a method can directly measure on the specimen without any dilution, concentration or other pretreatment
CRR= Clinical Reportable Range
Range of analyte values that a method can report as a quantitative result, allowing for specimen dilution, concentration, or other pretreatment used to expand the direct AMR.

37. System Flags Delta check
Comparison of individual patient results throughout the day or week with computer detection of changes from earlier individual patient results
Helpful to identify pre-analytical errors
Test
Change
Time Frame, hours
Other
Sodium, adult 7% 24
Creatinine
50% 72
Hemoglobin
3.0 g/dl 48
Transfusion/ Bleeding?

38. Establishment of Reference Ranges
Reference ranges – the ‘normals’
The normal or expected value for patients.
Are defined as being within +2 Standard Deviations from the mean
A large sampling of clinical normal representatives.
Each lab must establish its own reference ranges based on local population

39. Establishment of Reference Ranges
Factors affecting reference ranges:
Age
Sex
Diet
Medications
Physical activity
Pregnancy
Personal habits (smoking, alcohol)
Geographic location (altitude)
Body weight
Laboratory instrumentation (methodologies)
Laboratory reagents

40. Significance- low results Significance- high results
Test results
Critical values and read back of results
Values that indicate a life-threatening situation for the patient
Require notification of the value to nurse or physician
Nurse or physician must “read back” the results to the technician
Test
Results Decreased
Significance- low results
Results Increased
Significance- high results
Glucose, adult
< 50 mg/dL
Brain damage
>500 mg/dL
Diabetic coma
Sodium
<120 mEQ/L
Paralysis, arrhythmias
>160 mEQ/L
Dehydration, heart failure

41. References
Astles, J. R., Stang, H., & Alspach, T. (2013, September). CLIA requirements for proficiency testing: the basics for laboratory professionals. MLO, 45(9), 8-15.
Bishop, M., Fody, E., & Schoeff, l. (2010). Clinical Chemistry: Techniques, principles, Correlations. Baltimore: Wolters Kluwer Lippincott Williams & Wilkins.
Sunheimer, R., & Graves, L. (2010). Clinical Laboratory Chemistry. Upper Saddle River: Pearson .