IQC, IQC MATERIAL, QUANTITATIVE METHODS, Qualitative methods

1. Quality Control
Gift Ajay Sam
Lecturer-Quality Management
Department of Transfusion Medicine and immunohaematology
CMC, Vellore

2. Overview
• Quality Control (QC)
• QC material
• Quantative methodologies
• Control chart analysis
• Duplicate checking
• Delta check
• Moving averages
• Qualitative QC

3. Introduction
• Process control: refers to the controlling of activities in handling and
examination of samples.
• Quality Control (QC): It is a part of process control, designed to
monitor the analytic activities.
• ISO 9000:2000 (3.2.10): QC is part of quality management focussed of
fulfilling quality requirements.

4. Laboratory
testing
Quantative Qualitative Semi-Quantative

5. Goal of QC: Serve as an error
detection methodology and identify
causes of failure (due to test system,
operator or environment) before the
results are delivered to the patient.

6. Quantative-QC
• A control is a substance that contains an established amount of
substance being tested.
• Should mimic the original testing procedure.
• Should cover the complete clinical decision making points i.e. low to
high.
• Should contain the same matrix as the patient sample.
• Controls can be frozen, freeze dried or preserved by adding
preservative.
• Can be purchased or made at the laboratory.

8. Establishing value ranges for QC materials
• Control material should be assayed over a period of time
• At least 20 data points should be collected over a 20-30 day period.
• Calculate the mean and Standard deviation (SD)
• In case of 1 or 2 outliers they can be omitted while determining the
range (>2 outliers the procedure should be repeated).
• When the QC values are plotted on the X-axis and the frequencies on
the Y-axis they form a bell shaped curve around the mean. This is
called a Gaussian Distribution.

9. 68.3% within 1S
95.5% within 2S
99.7% within 2S

10. Control chart
• Shewart chart/ Statistical process chart
• Graph used to study how a process changes over time
• The central line indicates the mean/ average
• Upper and lower lines indicate the upper control limit and lower
control limits (+SD).

11. When to use a control chart
• Controlling an ongoing process and correcting problems as they occur.
• Analysing patterns of variations
• Ensuring process stability

12. 61554943373125191371
12.2
12.1
12.0
11.9
11.8
11.7
11.6
11.5
11.4
Observation
IndividualValue
_
X=11.77
UCL=12.04
LCL=11.5
+4SL=12.13
-4SL=11.41
+3SL=12.04
-3SL=11.5
+2SL=11.95
-2SL=11.59
+1SL=11.86
-1SL=11.68
1
I Chart of DX3_L2_HGB

13. What to look for?
• Random error: Caused by unexplainable but inherent fluctuation in
the reading
• Systematic error: Predictable, constant and proportional to the true
value.

14. Analysing the control chart
• Extremes: 13S
• Bias: 22s,41s,10X
• Trend: 6 values in an increasing or decreasing order
• On the edge: 21s

15. Random Duplicate sample testing
• Verify machine performance between shifts.
• Alternate assessment for QC.
• Any 10 processed samples from the previous shift selected.
• Processed after the shift is completed.
• Results of 1st and next run are tabulated

16. Delta checks
• Identify wrong blood in tube
• Compare present result with previous result
• If variation is clinically acceptable or not.
• Prior result not more than 36 hours before.
• The rate of change in test result within a given period of time.

17. Bulls Algorithm
• Dr. Brain S Bull, Loma Linda University, 1974
• Was replaced by multi-rule Shewart chart post 1985.
• QC based on RBC indices- MCV, MCH, MCHC
• Based on population normal
• Batch of 20 donor samples tested and mean compared to the
established Bull’s mean (study from samples obtained from 1,767
hospitals).
• If variation <0.5% acceptable.
• Variation of 1 run >3%; 3 consecutive batch >2%: QC failed, calibrate

18. Qualitative-QC
• Qualitative tests: Evaluate presence or absence of substance or assess
morphology.
• Semi-quantative: Provides an estimated measure of quantity. E.g. 1+,
2+, 0-5/ hpf etc.
• Control materials
• Built-in: integrated into the test system
• Traditional controls: Mimic patient sample e.g. positive control, negative
control.
• QC for stains: Checked daily with known controls, precipitate and crystal
formation, bacterial contamination.

19. References
• Lab Quality Management-Handbook, WHO, CLSI, CDC
• https://asq.org/quality-resources/control-chart
• http://www.westgard.com/westgard-rules-and-multirules.htm
• John Robert Taylor (1999). An Introduction to Error Analysis: The
Study of Uncertainties in Physical Measurements. University Science
Books. p. 94, §4.1
• Et, J. (1981). James O Wesgard et al, A Multi rule Shewart Chart for
Quality Control in Clinical Chemistry. Clin. Chem. 1981; 27/3. 493-501
(pp. 493–501). pp. 493–501.

20. References
• Carstairs, K. C., Peters, E., & Kuzin, E. J. (1977). Development and
description of the “random duplicates” method of quality control for
a hematology laboratory. American Journal of Clinical Pathology,
67(4), 379–385. https://doi.org/10.1093/ajcp/67.4.379
• Rosenbaum, M. W., & Baron, J. M. (2018). Using machine learning-
based multianalyte delta checks to detect wrong blood in tube errors.
American Journal of Clinical Pathology, 150(6), 555–566.
https://doi.org/10.1093/AJCP/AQY085
• Nosanchuk, J.S and Gottmann, A. W. (1974). CUMS and Delta Checks.
Am J Clin Pathol., 62(5), 707–712.

21. References
• Shagufta, S., Shettigar, S. K. G., Gujral, S., & Kulkarni, R. K. (2011). Bull
’ s Multirule Algorithm , ( M ), An Excellent Means of Internal Quality
Control for Hematology Analyzers Merit Award. (October), 351–355.

22. James O Westgard
Who introduced the Shewart-Chart in Clinical Chemistry

23. Delta Check
One strategy to identify wrong blood in a tube

24. Random duplicate testing
Strategy to reduce QC runs

25. Control chart
A plot used to detect changes over a period of time

26. Traditional control
Control material that mimic a patient sample