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Application of Geometric Mean Criteria for Anti- Blood Group Antibody Proficiency Testing in the Korean External Quality Assurance Program

Authors
양정석유희정정유선김형석황상현오흥범고대현
Issue Date
Mar-2022
Publisher
대한임상검사정도관리협회
Keywords
Laboratory proficiency testing; Geometric mean; Mode; Acceptability
Citation
Journal of Laboratory Medicine And Quality Assurance, v.44, no.1, pp 48 - 54
Pages
7
Indexed
KCI
Journal Title
Journal of Laboratory Medicine And Quality Assurance
Volume
44
Number
1
Start Page
48
End Page
54
URI
https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/55758
DOI
10.15263/jlmqa.2022.44.1.48
ISSN
2384-2458
2288-7261
Abstract
[한국어 초록 없음]
Background: The geometric mean criteria (GC) is an alternative for assessing proficiency testing (PT) acceptability. A recent study applied GC to the PT results provided by the College of American Pathologists. We assessed the feasibility of using GC for anti-blood group antibody titration testing (ABT) in the Korean PT program. Methods: The results of the ABT performed in 2019 were reviewed using GC. GC was calculated as geometric mean (GM)±multiples of geometric standard deviation (GSD). The number of acceptable results obtained using GC was compared to that of the conventional mode criteria (MC, mode±1 second). Only the results with 30 or more peer group responses were included in the analyses. Results: A total of 27 PT results (anti-A: 13, anti-B: 14) were analyzed. The acceptable proportions from MC were 82.9%–100.0% for anti-A and 76.2%– 100.0% for anti-B. The GC criteria yielded acceptable results of 46.9%–97.6% (1 GSD), 88.6%–100.0% (2 GSD), and 97.3%–100.0% (3 GSD) for anti-A. For anti-B, 1 GSD, 2 GSD, and 3 GSD criteria resulted in 44.7%–90.6%, 90.6%– 100.0%, and 97.4%–100.0%, respectively. In general, acceptable results using MC were found to be distributed between 1 GSD and 2 GSD. Conclusions: The GC can be used as an alternative assessment criterion with a more robust statistical rationale. While conventional MC struggles with representing the central tendency of data, GC provide better visualization of the central tendency.
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Yang, John Jeongseok
Guro Hospital (Department of Laboratory Medicine, Guro Hospital)
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