Practical laboratory medicine · 2025
Analytical performance evaluation of intelligent quality management of blood gas analyzer.
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Tang Hongting, Xiao Yawen, Luo Hong, Jiang Jian, Xu Hanqing, Yang Jun, Yang Lihua, Yang Xiang
Аннотация
OBJECTIVE: This study aimed to compare the application effectiveness and quality control (QC) performance of intelligent quality management for blood gas analysis (BGA) with those of traditional quality management.
METHODS: We implemented intelligent quality management by employing the GEM Premier 5000 equipped with Intelligent Quality Management 2 (iQM 2). By collecting external quality assessment (EQA) and internal quality control (IQC) data, we compared the clinical application outcomes and quality control (QC) performance between the intelligent management and traditional management approaches.
RESULTS: The average bias of EQA for pH, partial carbon dioxide pressure (pCO2), partial oxygen pressure (pO2), sodium (Na+) and calcium (Ca2+) decreased compared to pre-management levels; except for pO2, the average coefficient of variation (CV%) of intelligent QC was lower. The average estimated total error (TE) in the intelligent QC met the specified acceptance criterion. According to the average sigma and the goal index ratio (QGI), both QC modes have issues with accuracy and precision; the probabilities of false rejection (Pfr) of traditional QC and intelligent QC are almost the same; except for pO2 and Na+, the probability of error detection (Ped) of intelligent QC is greater, whereas the average detection time (ADT) of traditional QC is greater. In addition, intelligent QC identified errors in approximately 1.46 % of the samples.
CONCLUSIONS: The precision and accuracy of the BGA improved significantly compared to those before management, indicating significant advantages of intelligent quality management in quality management applications.
METHODS: We implemented intelligent quality management by employing the GEM Premier 5000 equipped with Intelligent Quality Management 2 (iQM 2). By collecting external quality assessment (EQA) and internal quality control (IQC) data, we compared the clinical application outcomes and quality control (QC) performance between the intelligent management and traditional management approaches.
RESULTS: The average bias of EQA for pH, partial carbon dioxide pressure (pCO2), partial oxygen pressure (pO2), sodium (Na+) and calcium (Ca2+) decreased compared to pre-management levels; except for pO2, the average coefficient of variation (CV%) of intelligent QC was lower. The average estimated total error (TE) in the intelligent QC met the specified acceptance criterion. According to the average sigma and the goal index ratio (QGI), both QC modes have issues with accuracy and precision; the probabilities of false rejection (Pfr) of traditional QC and intelligent QC are almost the same; except for pO2 and Na+, the probability of error detection (Ped) of intelligent QC is greater, whereas the average detection time (ADT) of traditional QC is greater. In addition, intelligent QC identified errors in approximately 1.46 % of the samples.
CONCLUSIONS: The precision and accuracy of the BGA improved significantly compared to those before management, indicating significant advantages of intelligent quality management in quality management applications.