Definitions
Quality Control - QC refers to the measures that must be included during each assay run to verify that the test is working properly.
Quality Assurance - QA is defined as the overall program that ensures that the final results reported by the laboratory are correct.
“The aim of quality control is simply to ensure that the results generated by the test are correct. However, quality assurance is concerned with much more: that the right test is carried out on the right specimen, and that the right result and right interpretation is delivered to the right person at the right time”
Quality Assessment - quality assessment (also known as proficiency testing) is a means to determine the quality of the results generated by the laboratory. Quality assessment is a challenge to the effectiveness of the QA and QC programs. Quality Assessment may be external or internal.
Variables that affect the quality of results
The educational background and training of the laboratory personnel
The condition of the specimens
The controls used in the test runs
Reagents
Equipment
The interpretation of the results
The transcription of results
The reporting of results
Errors in measurement
True value - this is an ideal concept which cannot be achieved.
Accepted true value - the value approximating the true value, the difference between the two values is negligible.
Error - the discrepancy between the result of a measurement and the true (or accepted true value).
Sources of error
Input data required - such as standards used, calibration values, and values of physical constants.
Instruments used - accuracy, repeatability.
Observer fallibility - reading errors, blunders, equipment selection, analysis and computation errors.
Environment - any external influences affecting the measurement.
Theory assumed - validity of mathematical methods and approximations.
Types of Errors in a Clinical Laboratory
Crude Error [all the pre-analytical errors]
Random Errors [all the sample & Technical errors]
Systematic Errors
Random Error
An error which varies in an unpredictable manner, in magnitude and sign, when a large number of measurements of the same quantity are made under effectively identical
conditions.
Random errors create a characteristic spread of results for any test method and cannot be accounted for by applying corrections. Random errors are difficult to eliminate but repetition reduces the influences of random errors.
Examples of random errors include errors in pipetting and changes in incubation period. Random errors can be minimized by training, supervision and adherence to standard operating procedures.
Random Errors – these errors affect the reproducibility or precision of a test system.
Usually 13S or R4S rules can be due to variations in line voltage, pipettes, dispensers,contamination, volume dispensed, bubbles in lines of reagents, etc.
Systematic Error
An error which, in the course of a number of measurements of the same value of a given quantity, remains constant when measurements are made under the same conditions,or varies according to a definite law when conditions change.
Systematic errors create a characteristic bias in the test results and can be accounted for by applying a correction.
Systematic errors may be induced by factors such as variations in incubation temperature, blockage of plate washer, change in the reagent batch or modifications in testing method.
Systematic Errors – (bias, shifts and trends) – these errors affect the accuracy of the test system.
Usually 22S, 41S, or 10x rules can be due to calibration lot changes,temperature changes in incubator unit, light source deterioration, electronics, reagent lot changes,
etc.
Shewhart Control Charts
A Shewhart Control Chart depend on the use of IQC specimens and is developed in the following manner:-
Put up the IQC specimen for at least 20 or more assay runs and record down the O.D. with the corresponding dates.
Calculate the mean and standard deviations (s.d.)
Make a plot with the assay run on the x-axis, and O.D.on the y axis.
Draw the following lines across the y-axis: mean, -3, -2, -2, 1, 2 and 3 s.d.
Plot the O.D./cut-off obtained for the IQC specimen for subsequent assay runs
Major events such as changes in the batch no. of the kit and instruments used should be recorded on the chart.
Westgard rules
The formulation of Westgard rules were based on statistical methods. Westgard rules are commonly used to analyse data in Shewhart control charts.
Westgard rules are used to define specific performance limits for a particular assay and can be use to detect both random and systematic errors.
There are six commonly used Westgard rules of which three are warning rules and the other three mandatory rules.
The violation of warning rules should trigger a review of test procedures, reagent performance and equipment calibration.
The violation of mandatory rules should result in the rejection of the results obtained with patients’ serum samples in that assay.
Follow-up action in the event of a violation
There are three options as to the action to be taken in the event of a violation of a Westgard rule:
Accept the test run in its entirety - this usually applies when only a warning rule is violated.
Reject the whole test run - this applies only when a mandatory rule is violated.
Enlarge the greyzone and thus re-test range for that particular assay run - this option can be considered in the event of a violation of either a warning or mandatory rule.
Typical Rule Combinations
For controls run in multiples of 2 (typically chemistry)
13S / 22S / R4S / 41S / 10X
For controls run in multiples of 3 (typically hematology, coagulation, blood gases)
13S / 2of 32S / R4S / 31S / 12X
The Westgard Rules
The main rues a Clinical Laboratory Follows;
12S
13S
22S
R4S
41S
10x
12S–refers to the historical rule of plus/minus 2s from the mean-with multi-rules: a warning rule to trigger careful inspection of control data
13S-refers to plus/minus 3s a run is rejected when a single control exceeds the mean ± 3s
22S – reject the run when 2 consecutive controls exceed the mean ± 2s
R4S – when 1 control in a group exceeds the mean ± 2s and another control exceeds the mean in the other direction by 2s then reject run
41S – when 4 consecutive control measurements are on one side of the mean either ± 1s Warning rule or a rejection rule depending on the accuracy of your instrument.
10x – 10 consecutive control measurements fall on one side of the mean
a) If within 1 s, warning
b) If between 1 and 2 s, reject
c) 2of 32S – reject the run when 2 of 3 controls exceed the mean ± 2s
• 9x – reject when 9 consecutive control measurements fall on one side of the mean
• 7T – reject when seven control measurements trend in the same direction, either higher or lower
Warning rules
Warning 12SD : It is violated if the IQC value exceeds the mean by +/-2SD. It is an event likely to occur normally in less than 5% of cases.
Warning 22SD : It detects systematic errors and is violated when two consecutive IQC values exceed the mean on the same side of the mean by +/-2SD.
Warning 41SD : It is violated if four consecutive IQC values exceed the same limit (mean +/-1SD) and this may indicate the need to perform instrument maintenance or reagent calibration.
Mandatory rules
Mandatory 13SD : It is violated when the IQC value exceeds the mean by +/-3SD. The assay run is regarded as out of control.
Mandatory R4SD : It is only applied when the IQC is tested in duplicate. This rule is violated when the difference in SD between the duplicates exceeds 4SD.
Mandatory 10x : This rule is violated when the last 10 consecutive IQC values are on the same side of the mean or target value.
Accuracy –vs- Precision
Accuracy – how close you are to the correct value
Precision – how close together your results are to each other
Lab should Define a QC Protocol
Each lab needs to define its’ QC protocol based on the number of controls used, the accuracy of the instrumentation, the total allowable error,etc.
QC Protocol - example
1. Statistical QC Procedure
a) Use a 12S as a warning rule and the 13S / 22S / R4S / 41S / 10X as rejection rules with 2 control measurements
2. Analyze control materials
a) Analyze 1 sample of each level of control.
3. Interpretation of warning rules
a) If both control results are within 2s, report the results.
If one control exceeds a 2s limit,follow flow chart and if any rule is violated,reject run.
4.Within run inspection
a) Inspect control results by applying rules: 13S in each run and 22S and R4S across levels.
5. Inspect controls across runs
a) Apply the 22S rule with each level across the last two runs.
b) Apply the 41S rule within each control level across the last 4 runs and across the last 2 runs of both levels.
6. If none of the rules are violated, accept the run.
Problem Solving
If a run is out of control, investigate the process and correct the problem
Do not automatically repeat the control!
What do you need to do to investigate the process?
Determine the type of error based on your rule violation (random or systematic)
Relate the type of error to the potential cause
Inspect the testing process and consider common factors on multi-test systems
Relate causes to recent changes
Verify the solution and document the corrective action
The records we need
Instrument Information & Validation
Reportable range (linearity)
Precision and Accuracy studies
Analytical sensitivity / specificity
Reference range
Proficiency testing results
Reagent logs
Problem logs
QC Documents / Logs
Preventative maintenance
Scheduled and unscheduled
Reason for maintenance
Frequency and length of downtime
Signs of instrument deterioration
Lot numbers and expiry of calibrators, dates of calibration, reason for calibration/verification, and by whom
Instrument function and temperature checks
Previous Control runs
All of these documents can be helpful when investigating errors!
Why use Westgard Rules?
We use Westgard Multi-rules to help us reduce costs while maintaining a high level of certainty that our analytical process is functioning properly.
In other words to diminish the false rejection rate without compromising quality.
Courtesy: Westgard website
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