Clustering of patient-derived laboratory data: A privacy-preserving distributed approach to enhance clinical laboratory quality control and regional external quality assessment.

Authors

Chao Song, Qian Chen, Chunsheng Qu, Liqing Xu, Zhiming Shan, Jun Zhou, Weixing Li

Published in

PloS one. Volume 21. Issue 10. Pages e0359083. Epub Oct 16, 2026.

Abstract

Traditional quality control (QC) has inherent limitations, and there are still various challenges existing in patient-based real-time QC (PBRTQC) and patient-based quality assurance (PBQA). We developed an integrated PBRTQC-PBQA model using density-based clustering and evaluated its effectiveness as a regional clinical laboratory quality assurance tool.
A 20-month multicentre study across seven hospitals analysed five haematological analytes(white blood cell count [WBC], red blood cell count [RBC], haemoglobin [HGB], platelet count [PLT], and mean corpuscular volume [MCV]). Custom Clustering-PBRTQC-PBQA(CPP) models were developed using log/Box-Cox transformation, 3D spatial construction with companion analytes determined by distribution morphology, Mahalanobis distance filtering (chi-square = 0.3, ~ 47% exclusion rate), and iterative OPTICS clustering to identify hospital-specific core reference populations. A privacy-preserving distributed architecture transmitted only aggregated statistical summaries. Median/EWMA harts (PBRTQC) and Z-score/relative bias charts (PBQA) were created alongside a closed-loop quality improvement system and calibration event verification.
The CPP model reduced EWMA alert episodes by 25.0% (92-69) while increasing 1-3S alert episodes by 31.2% (64-84) compared with conventional moving-average PBRTQC, demonstrating selective elimination of systematic bias signals while preserving sensitivity to random errors (net change: 180-173 episodes, -3.9%). Overall calibration consistency was 82.3% (95% CI: 74.1%-88.7%). Among 52 confirmed true-positive alerts, 37 (71.2%) were detected without concurrent internal QC out-of-control signals, demonstrating the model's capacity to capture low-magnitude biases that conventional IQC misses.
Density-based clustering underpins the PBRTQC-PBQA model's effectiveness in deriving hospital-specific core reference values, filtering unconfirmed QC alerts, validating calibration, and enhancing regional test consistency. The model's ability to detect systematic biases not captured by IQC underscores its value as a complementary quality assurance layer. Its lightweight design supports regional deployment.

PMID:
42886680
Bibliographic data and abstract were imported from PubMed on 17 Oct 2026.

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