Choosing quality-control systems to detect maximum clinically allowable analytical errors

Area of Science:

• Clinical Chemistry
• Laboratory Medicine
• Analytical Science

Background:

• Clinical laboratories rely on robust quality control (QC) systems to ensure the accuracy and reliability of patient test results.
• Analytical errors in clinical chemistry can significantly impact patient diagnosis and treatment decisions.
• Existing QC rules are sometimes debated regarding their restrictiveness and effectiveness.

Purpose of the Study:

• To estimate critical analytical errors for 17 common clinical chemical analytes.
• To determine appropriate quality control systems based on statistical power considerations.
• To evaluate the adequacy of current quality control rules in clinical laboratories.

Main Methods:

• Estimation of critical systematic and random analytical errors using published data on analytical imprecision, biological variation, and medically important changes.
• Application of power considerations to assess the effectiveness of different quality control rules (e.g., 1(3)s, 1(2)s).
• Analysis of analytes requiring different levels of quality control stringency based on error magnitudes.

Main Results:

• The 1(3)s rule with one control per run is minimally sufficient for about 25% of analytes where critical error is at least 3 analytical standard deviations (SDs).
• The 1(2)s rule with one control per run is the minimal requirement for approximately 50% of the remaining analytes with critical errors around 2 SDs.
• More powerful quality control is achievable using multiple rules and several controls per run.

Conclusions:

• The evaluated quality control rules, such as 1(3)s and 1(2)s, are often adequate for managing analytical errors in clinical chemistry.
• The study does not support claims that current quality control rules are excessively restrictive.
• Tailoring QC strategies to specific analytes based on their error characteristics is crucial for efficient laboratory operations.