Establishing Quality Control Limits

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The page below is a sample from the LabCE course Quality Control. Access the complete course and earn ASCLS P.A.C.E.-approved continuing education credits by subscribing online.

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Establishing Quality Control Limits

Whether you use assayed controls or unassayed controls, you will need to calculate acceptable ranges for each lot of control materials. Sufficient data points must be collected to guarantee that the calculations are valid and provide assurance that patient test results are as accurate as possible.
In order for the data to reflect future system performance, it is important that the measurements be made under conditions that are representative of the normal variation encountered during the course of daily testing.
Possible variations in testing include:
  • Changing calibrator lots
  • Performing calibrations
  • Changing reagent lots
  • Changing bottles of control material
  • Changing operators
  • Data collected at different times of the day
  • Changes in sample probes or pipettes
  • Post-maintenance changes
Data should NOT be collected during abnormal or out-of-control conditions.
All valid data points should be used in calculations or the control limits may become too restrictive. This would not reflect the actual variability of the test system. Including quality control data from afternoon and night shifts makes sure you cover all results and all employees in your quality control program. Quality control results that exceed + 2 standard deviations (SD) should not be deleted when you are establishing QC limits as this can distort or skew the limits. However, if more than two data points appear to be significantly higher or lower than the rest of the data, this could be an indication of a problem. In this case, you should resolve the problem and recollect the data.
Using control data from the previous month can help you predict what may occur this month, thus helping your calculations.
Using target values suggested by the manufacturer provides an example of the likely values that will be encountered.