Trend detection in control data: optimization and interpretation of Trigg's technique for trend analysis

Area of Science:

• Clinical Chemistry
• Laboratory Medicine
• Statistical Process Control

Background:

• Multitest continuous-flow analyzers generate substantial control data requiring robust monitoring.
• Traditional trend detection methods may not be optimal for complex analytical systems.
• Effective trend detection is crucial for maintaining analytical accuracy and patient safety.

Purpose of the Study:

• To evaluate Trigg's technique for monitoring trends in control data from multitest continuous-flow analyzers.
• To optimize Trigg's method using simulated data and assess its accuracy with real-world data.
• To establish criteria for interpreting Trigg's trend data and facilitate computer implementation.

Main Methods:

• Application of Trigg's trend detection technique to simulated and retrospective control data.
• Optimization of Trigg's method parameters using simulated datasets.
• Analysis of actual control data to determine trend frequency and parameter accuracy.
• Development of interpretation criteria and a computational algorithm for Trigg's method.

Main Results:

• Trigg's technique demonstrated successful identification of significant trends in control data.
• Optimization using simulated data improved the method's performance.
• Retrospective analysis provided insights into trend frequency and parameter reliability.
• Prospective application confirmed the technique's ability to uncover important trends.

Conclusions:

• Trigg's technique is a valuable tool for monitoring trends in multitest continuous-flow analyzer control data.
• The study provides practical guidelines and computational tools for implementing Trigg's method.
• This approach enhances the reliability and efficiency of laboratory quality control processes.