The multi-center validation of an intelligent blood gas analyzer quality management system

Abstract

Objective To compare and study the two kinds of quality control methodologies related to intelligent quality management system (iQM) and traditional quality control, and the quality control performance of iQM equivalent to traditional quality control were evaluated, ensuring the accuracy of the results of blood gas testing. Methods Beijing Chaoyang Hospital of Capital Medical University, Beijing Tiantan Hospital of Capital Medical University, Shanghai Longhua Hospital of Shanghai University of Chinese Medicine, and Sichuan Provincial People′s Hospital, these 4 medical institutions were selected to implement this study. During the period from June 2016 to December 2016, in the routine detection of total 3 712 specimen, the iQM and traditional quality control modes were used simultaneously to calculate the mean values of all blood gas parameters quality controls, SD, CV (%) and Sigma values, to evaluate the quality control performance and difference of the two quality control modes. Results During the process of testing blood gas samples from 3 712 specimen in 4 hospitals, iQM process control solution (PCS) A, B, C ran 1 089, 7 678 and 154 quality control samples respectively, and 732 external quality control samples were run by traditional quality control mode. Considering the most sensitive parameters of blood gas testing pO2, iQM PCS A, B, C′s Sigma value are higher than 8, however, the traditional quality control′s Sigma value are less than 6; For parameters pCO2, pO2 and Na+ , there exists significant difference between two quality control methods (P=0.004 8, P=0.000 1, P=0.004 4, P 0.01). Totally 22 errors detected by iQM, includes 14 micro-clots and 8 interferences samples, which were not detected by traditional quality control . Conclusions The error in blood gas analysis mainly comes from the pre-analytical phase. iQM enhanced specimen inspection capabilities and make up for the inability of traditional quality control to monitor the quality of specimens, enabling full-scale, real-time, and dynamic monitoring of each specimen, powerful error detection capabilities, and automatic error correction capabilities. Besides, automatic documentation saves staff much time. The system can effectively ensure the accuracy of blood gas test results, meet the quality requirements of related laws and regulations and related industry standards, and also can meet the clinical intended use, providing new ideas for POCT quality management and improvement.(Chin J Lab Med, 2018, 41: 475-480) Key words: Blood Gas; Point of Care Testing; Quality Control; Intelligent Quality Management