An approach to selecting auto-verification limits and validating their error detection performance independently for...

Area of Science:

• Clinical Chemistry
• Laboratory Medicine
• Quality Control

Background:

• Auto-verification limits are crucial for detecting pre-analytical, analytical, and post-analytical errors in laboratories.
• A novel approach is detailed for validating auto-verification limit performance in a lab-specific manner, separately for pre-analytical and analytical error detection.

Purpose of the Study:

• To evaluate a new method for validating auto-verification limit performance.
• To assess the efficacy of upper-limit checks (ULC) and lower-limit checks (LLC) in detecting laboratory errors.

Main Methods:

• Error-detection simulations were performed using MA Generator with various ULC and LLC.
• Pre-analytical error detection was defined as triggering an alarm within one erroneous result.
• Analytical error detection was defined as triggering an alarm within the scheduled internal quality control (QC) measurement interval with ≥97.5% probability.

Main Results:

• Limit checks demonstrated superior pre-analytical error detection and rapid detection of larger analytical errors compared to moving average quality control.
• This improved detection came at the cost of a significantly higher alarm rate.
• Specific ULC and LLC thresholds were determined for hemoglobin, sodium, and calcium, achieving high error detection probabilities.

Conclusions:

• The developed method allows for substantiated selection and validation of optimal auto-verification limits.
• The performance of ULC and LLC in terms of alarm rates and error detection provides a basis for laboratory-specific limit setting.