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Internal quality control: Moving average algorithms outperform Westgard rules.

Daren Kiat How Poh1, Chun Yee Lim2, Rui Zhen Tan2

  • 1MD Programme Department, Duke-NUS Medical School, Singapore.

Clinical Biochemistry
|September 17, 2021
PubMed
Summary
This summary is machine-generated.

Moving average algorithms, including simple moving average (SMA), demonstrate superior systematic error detection in internal quality control (IQC) compared to traditional Westgard rules. These algorithms offer a simpler IQC strategy for laboratories.

Keywords:
Laboratory informaticsMoving averageQuality assuranceQuality controlQuality management

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Area of Science:

  • Clinical Chemistry
  • Laboratory Medicine
  • Quality Control

Background:

  • Internal quality control (IQC) traditionally relies on Westgard rules for interpreting data against control limits.
  • Commonly used Westgard rules (e.g., 1:3s, 2:2s) have limitations in detecting systematic errors.
  • This study investigates alternative methods for enhanced systematic error detection in IQC.

Purpose of the Study:

  • To directly compare the performance of three moving average algorithms against Westgard rules for systematic error detection.
  • To evaluate the sensitivity and efficiency of moving average algorithms in identifying systematic errors in IQC data.

Main Methods:

  • A simulation study generated error-free IQC data.
  • Westgard rules (1:3s, 2:2s) and three moving average algorithms (simple moving average (SMA), weighted moving average (WMA), exponentially weighted moving average (EWMA)) were applied.
  • Systematic errors were introduced to assess error detection probability and average number of episodes for error detection (ANEed).

Main Results:

  • All three moving average algorithms exhibited a higher probability of detecting systematic errors compared to Westgard rules.
  • Moving average algorithms demonstrated a lower average number of episodes needed for error detection (ANEed).
  • False positive rates were comparable across all methods, remaining below 0.5%.

Conclusions:

  • Simple moving average (SMA) algorithms enhance systematic error detection in IQC compared to Westgard rules.
  • Implementing SMA algorithms can streamline laboratory IQC strategies.
  • Moving average algorithms provide a more sensitive approach to identifying systematic errors in laboratory testing.