A New Strategy for Evaluating the Quality of Laboratory Results for Big Data Research: Using External Quality Assessment Survey Data (2010-2020)

被引:8
|
作者
Cho, Eun-Jung [1 ]
Jeong, Tae-Dong [2 ]
Kim, Sollip [3 ,4 ]
Park, Hyung-Doo [5 ]
Yun, Yeo-Min [6 ]
Chun, Sail [3 ,4 ]
Min, Won-Ki [3 ,4 ]
机构
[1] Hallym Univ, Dongtan Sacred Heart Hosp, Dept Lab Med, Coll Med, Hwaseong, South Korea
[2] Ewha Womans Univ, Dept Lab Med, Coll Med, Seoul, South Korea
[3] Univ Ulsan, Coll Med, Dept Lab Med, 88 Olymp Ro 43 Gil, Seoul 05505, South Korea
[4] Asan Med Ctr, 88 Olymp Ro 43 Gil, Seoul 05505, South Korea
[5] Sungkyunkwan Univ, Samsung Med Ctr, Dept Lab Med & Genet, Sch Med, Seoul, South Korea
[6] Konkuk Univ, Med Ctr, Dept Lab Med, Sch Med, Seoul, South Korea
关键词
Key Words; Bias; Big data; Biological variation; Data quality; External quality assessment; SERUM CREATININE MEASUREMENT; HEPATOCELLULAR-CARCINOMA; STANDARDIZATION; CHOLESTEROL; PERFORMANCE; CALIBRATION; DISEASE; PSA;
D O I
10.3343/alm.2023.43.5.425
中图分类号
R446 [实验室诊断]; R-33 [实验医学、医学实验];
学科分类号
1001 ;
摘要
Background: To ensure valid results of big data research in the medical field, the input laboratory results need to be of high quality. We aimed to establish a strategy for evaluat-ing the quality of laboratory results suitable for big data research. Methods: We used Korean Association of External Quality Assessment Service (KEQAS) data to retrospectively review multicenter data. Seven measurands were analyzed using commutable materials: HbA1c, creatinine (Cr), total cholesterol (TC), triglyceride (TG), al-pha-fetoprotein (AFP), prostate-specific antigen (PSA), and cardiac troponin I (cTnI). These were classified into three groups based on their standardization or harmonization status. HbA1c, Cr, TC, TG, and AFP were analyzed with respect to peer group values. PSA and cTnI were analyzed in separate peer groups according to the calibrator type and manufacturer, respectively. The acceptance rate and absolute percentage bias at the medical decision level were calculated based on biological variation criteria. Results: The acceptance rate (22.5%-100%) varied greatly among the test items, and the mean percentage biases were 0.6%-5.6%, 1.0%-9.6%, and 1.6%-11.3% for items that satisfied optimum, desirable, and minimum criteria, respectively. Conclusions: The acceptance rate of participants and their external quality assessment (EQA) results exhibited statistically significant differences according to the quality grade for each criterion. Even when they passed the EQA standards, the test results did not guarantee the quality requirements for big data. We suggest that the KEQAS classification can serve as a guide for building big data.
引用
收藏
页码:425 / 433
页数:9
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