Analytical performance evaluation of intelligent quality management of blood gas analyzer

Area of Science:

• Clinical Chemistry
• Medical Diagnostics
• Laboratory Quality Management

Background:

• Traditional quality control (QC) methods in blood gas analysis (BGA) face challenges in accuracy and efficiency.
• The implementation of intelligent quality management systems offers a potential solution to enhance BGA QC performance.

Purpose of the Study:

• To compare the application effectiveness and QC performance of intelligent quality management against traditional quality management in BGA.
• To evaluate the impact of intelligent quality management on key BGA analytes and overall analytical quality.

Main Methods:

• Intelligent quality management was implemented using the GEM Premier 5000 with Intelligent Quality Management 2 (iQM 2).
• External quality assessment (EQA) and internal quality control (IQC) data were collected and analyzed.
• Performance metrics including bias, coefficient of variation (CV%), estimated total error (TE), sigma, goal index ratio (QGI), probability of false rejection (Pfr), probability of error detection (Ped), and average detection time (ADT) were compared.

Main Results:

• Average bias for EQA analytes (pH, pCO2, Na+, Ca2+) decreased, and intelligent QC showed lower average CV% (except for pO2).
• Intelligent QC met the acceptance criterion for average TE. While both QC modes had accuracy/precision issues, intelligent QC demonstrated greater Ped and identified errors in 1.46% of samples.
• Intelligent QC had a greater probability of error detection (Ped) for most analytes and a shorter average detection time (ADT) compared to traditional QC.

Conclusions:

• Intelligent quality management significantly enhanced the precision and accuracy of BGA.
• The study highlights the advantages of intelligent quality management systems in improving BGA quality control applications.