Tang Hongting, Xiao Yawen, Luo Hong, Jiang Jian, Xu Hanqing, Yang Jun, Yang Lihua, Yang Xiang
Department of Clinical Laboratory, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, 400038, PR China.
Pract Lab Med. 2025 May 29;45:e00480. doi: 10.1016/j.plabm.2025.e00480. eCollection 2025 Jul.
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.
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.
The average bias of EQA for pH, partial carbon dioxide pressure (pCO), partial oxygen pressure (pO), sodium (Na) and calcium (Ca) decreased compared to pre-management levels; except for pO, 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 pO 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.
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.
本研究旨在比较血气分析（BGA）智能质量管理与传统质量管理的应用效果及质量控制（QC）性能。
我们通过使用配备智能质量管理2（iQM 2）的GEM Premier 5000实施智能质量管理。通过收集外部质量评估（EQA）和内部质量控制（IQC）数据，我们比较了智能管理和传统管理方法之间的临床应用结果和质量控制（QC）性能。
与管理前水平相比，EQA中pH、二氧化碳分压（pCO）、氧分压（pO）、钠（Na）和钙（Ca）的平均偏差降低；除pO外，智能QC的平均变异系数（CV%）较低。智能QC中的平均估计总误差（TE）符合指定的验收标准。根据平均西格玛和目标指标比（QGI），两种QC模式在准确性和精密度方面都存在问题；传统QC和智能QC的假拒收概率（Pfr）几乎相同；除pO和Na外，智能QC的误差检测概率（Ped）更大，而传统QC的平均检测时间（ADT）更长。此外，智能QC在约1.46%的样本中识别出误差。
与管理前相比，BGA的精密度和准确性有显著提高，表明智能质量管理在质量管理应用中具有显著优势。
