ArticleJournal of clinical laboratory analysis2022
Assessment of patient based real-time quality control on comparative assays for common clinical analytes.
Article in Journal of clinical laboratory analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A comparative study of the quality control efficacy of multiple error-introduction methods for patient-based real-time quality control.Practical laboratory medicine · 2026Article
- A Comparative Study of the Clinical Laboratory Quality Control Performance of AI-PBRTQC and Traditional PBRTQC Model in Tumor Marker Testing.Diagnostics (Basel, Switzerland) · 2026Article
- Longitudinal Trends and Analytical Consistency of Folate and Vitamin BBiomedicines · 2026Article
- A longitudinal analysis of population-level lipid and apolipoprotein trends over two decades: descriptive assessment using patient medians in a Swedish tertiary care center.Lipids in health and disease · 2025Article
- Patient-based pre-classified real-time quality control with neural network (PCRTQC-NN).Practical laboratory medicine · 2025Article
- Comparative study on the quality control effectiveness of AI-PBRTQC and traditional PBRTQC model in identifying quality risks.Biochemia medica · 2024Article
- Application of Patient-Based Real-Time Quality Control Based on Artificial Intelligence Monitoring Platform in Continuously Quality Risk Monitoring of Down Syndrome Serum Screening.Journal of clinical laboratory analysis · 2024Article
- Assessment of patient based real-time quality control on comparative assays for common clinical analytes.Journal of clinical laboratory analysis · 2022Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundIt is critical for laboratories to conduct multianalyzer comparisons as a part of daily routine work to strengthen the quality management of test systems. Here, we explored the application of patient-based real-time quality controls (PBRTQCs) on comparative assays to monitor the consistency among clinical laboratories.
methodsThe present study included 11 commonly tested analytes that were detected using three analyzers. PBRTQC procedures were set up with exponentially weighted moving average (EWMA) algorithms and evaluated using the AI-MA artificial intelligence platform. Comparative assays were carried out on serum samples, and patient data were collected. Patients were divided into total patient (TP), inpatient (IP), and outpatient (OP) groups.
resultsOptimal PBRTQC protocols were evaluated and selected with appropriate truncation limits and smoothing factors. Generally, similar comparative assay performance was achieved using both the EWMA and median methods. Good consistency between the results from patients' data and serum samples was obtained, and unacceptable bias was detected for alkaline phosphatase (ALP) and gamma-glutamyl transferase (GGT) when using analyzer C. Categorizing patients' data and applying specific groups for comparative assays could significantly improve the performance of PBRTQCs. When monitoring the inter- and intraanalyzer stability on a daily basis, EWMA was superior in detecting very small quality-related changes with lower false-positive alarms.
conclusionsWe found that PBRTQCs have the potential to efficiently assess multianalyzer comparability. Laboratories should be aware of population variations concerning both analytes and analyzers to build more suitable PBRTQC protocols.
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