协调
医学
四分之一(加拿大硬币)
统计
人口学
医学物理学
数学
地理
声学
物理
社会学
考古
作者
Beili Wang,Yiwen Zhou,Jiong Wu,Wei Shao,Wen Xu,Wei Guo
标识
DOI:10.3760/cma.j.issn.1009-8158.2020.01.003
摘要
Objective
To review the results of inter-laboratory comparisons in Shanghai glycohemoglobin harmonization program from 2010 to 2018, and to analyze the evolution of quality levels of HbA1c determination, so as to provide the reference for improving the HbA1c determination quality in China.
Methods
Retrospective analysis. The comparison data of Shanghai Glycohemoglobin Harmonization Program from 2010 to 2018 was collected. And the change trend was analyzed about hospital and determination method distribution. The judgment criteria, quarterly and annual pass rate, bias and coefficient of variation of the results of the inter-laboratory comparison were analyzed retrospectively, and the results were compared with the results of External Quality Assessment Programme carried out by the National Center for Clinical Laboratories, Shanghai Center for Clinical Laboratories and College of American Pathologists (CAP). The data in the first quarter of 2019 was collected and the imprecision, bias and sigma were calculated, which were drew in the evaluation model of sigma combined with biomedical variation parameters.
Results
The number of participating laboratories increased from 9 in Shanghai to 192 in the whole country, with an average annual growth rate of 76.6%. The quarterly comparison improved from ±8% to ±6% and the passing rate of participating laboratories increased from 39.1% to nearly 90%. The maximum CV of each instrument among laboratories decreased from 14.3% to 4.8%. In the first quarter of 2019, nearly 60% of the laboratories met 6σcriteria and more than 95% of the laboratories met the standard criteria in the model of biological variation parameters.
Conclusion
Shanghai Glycohemoglobin harmonization program has improved the harmonization of HbA1c test results among the participating laboratories.
Key words:
Diabetes mellitus; Glycated hemoglobin A; Reproducibility of results
科研通智能强力驱动
Strongly Powered by AbleSci AI