ArticleAnnals of laboratory medicine2023
A New Strategy for Evaluating the Quality of Laboratory Results for Big Data Research: Using External Quality Assessment Survey Data (2010-2020).
Article in Annals of laboratory medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
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- Intra-Laboratory and Inter-Laboratory Variations Analysis for HbA1c Assays in China: Using Internal Quality Control and External Quality Assessment Data.Journal of clinical laboratory analysis · 2025Article
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- Customized Quality Assessment of Healthcare Data.Annals of laboratory medicine · 2024Article
- Laboratory Preparation for Digital Medicine in Healthcare 4.0: An Investigation Into the Awareness and Applications of Big Data and Artificial Intelligence.Annals of laboratory medicine · 2024Article
- Quantitative Evaluation of the Real-World Harmonization Status of Laboratory Test Items Using External Quality Assessment Data.Annals of laboratory medicine · 2024Article
- Laboratory Data Quality Evaluation in the Big Data Era.Annals of laboratory medicine · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 evaluating 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), alpha-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 all 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.