Selecting Statistical Quality Control Procedures for Limiting the Impact of Increases in Analytical Random Error on...

Area of Science:

• Clinical Chemistry
• Laboratory Medicine
• Quality Management

Background:

• Risk management in quality control (QC) planning can prevent patient harm from undetected errors.
• Selecting appropriate QC procedures reduces the reporting of erroneous results.
• Nomograms facilitate QC rule selection for systematic errors, but random errors pose a detection challenge.

Purpose of the Study:

• To develop statistical models and charts for QC rules (1_ks and X/χ²).
• To assess the impact of random error increases on reported patient results.
• To enable QC planning based on the risk of patient harm from erroneous results.

Main Methods:

• Statistical modeling was employed to construct performance charts for specific QC rules.
• Charts were developed to correlate increased random error with measurement procedure capability.
• The models assess the risk of reporting unacceptable patient results.

Main Results:

• 1_ks rules are versatile and minimally impacted by stable between-run imprecision.
• X/χ² rules show improved performance with an increased number of controls per QC event.
• Charts effectively relate random error increases to QC procedure capability.

Conclusions:

• Nomograms simplify the selection of statistical QC procedures to minimize erroneous results from random error.
• The choice of QC procedure is influenced by the presence or absence of stable between-run imprecision.
• Risk-based QC planning aids in reducing patient harm.