A Two-Tier Patient-Based Real-Time Quality Control Program Integrating K-Means Department Stratification and Inter-Instrument Ratios Improves Detection of Small Systematic Errors in Creatinine Testing.
- 1Department of Clinical Laboratory, The First Medical Center of Chinese PLA General Hospital, Beijing 100853, China.
- 2Department of Clinical Laboratory, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China.
Abstract
Background/Objectives: Patient-based real-time quality control (PBRTQC) complements traditional internal quality control. This study validates a ratio-based PBRTQC integrating department clustering with inter-instrument ratio monitoring. Methods: Creatinine data (384,730 cases, 2025) from three measuring systems (A, B, C) were analyzed; conventional PBRTQC on unstratified Instrument A data served as comparator. Departments were stratified by K-means clustering of annual mean/SD, keeping the low-mean cluster (124 departments). The data of each measuring system were split chronologically: first-layer PBRTQC was trained on the first part, the second trained the ratio programs, and the third provided independent validation. Three systematic errors (fixed additive offsets of +5.28 and +10.56 μmol/L, and a progressive offset rising to +10.56 μmol/L, corresponding to 6% and 12% of the first-layer action limit) were simulated to assess the true positive rate (TPR), false positive rate (FPR), median samples to detection (MNPed), detection time and diagnostic odds ratio (DOR). Results: Under the fixed +5.28 μmol/L (6%) offset, A-B TPR was 28.83% versus 6.77% for traditional PBRTQC, with MNPed reduced from 34 to 20. Under the progressive offset rising to +10.56 μmol/L (12%), A-B TPR was 29.00% versus 8.35%, and detection time fell from 20 to 14 min. Under the fixed +10.56 μmol/L (12%) offset, A-B and A-C TPR reached 70.15% and 74.72% (MNPed 8 and 5; detection time 2 and 1 min). Across all scenarios the ratio programs achieved higher DOR. A stratified single-instrument comparator showed complementary contributions of stratification and ratio monitoring (TPR at the +5.28 μmol/L offset: 6.77%, 15.61% and 28.83%). Conclusions: Integrating department stratification with ratio-based PBRTQC markedly improves detection of small systematic errors in creatinine testing, albeit with more false positives, offering a transferable framework for laboratories using multiple same-platform measuring systems.
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BibTeX
@article{wei2026twotier,
title = {{A Two-Tier Patient-Based Real-Time Quality Control Program Integrating K-Means Department Stratification and Inter-Instrument Ratios Improves Detection of Small Systematic Errors in Creatinine Testing.}},
author = {Xuemei Wei and Ziqing Jiang and Jingzhu Nan and Zefeng Yan and Zhu Lin and Rong Li and Jie Feng and Yanhong Gao},
journal = {Diagnostics (Basel, Switzerland)},
year = {2026},
month = oct,
doi = {10.3390/diagnostics16193238},
url = {https://europepmc.org/article/MED/42882393},
note = {CC BY},
}
RIS
TY - JOUR
TI - A Two-Tier Patient-Based Real-Time Quality Control Program Integrating K-Means Department Stratification and Inter-Instrument Ratios Improves Detection of Small Systematic Errors in Creatinine Testing.
AU - Xuemei Wei
AU - Ziqing Jiang
AU - Jingzhu Nan
AU - Zefeng Yan
AU - Zhu Lin
AU - Rong Li
AU - Jie Feng
AU - Yanhong Gao
PY - 2026
DA - 2026/10/07
JO - Diagnostics (Basel, Switzerland)
DO - 10.3390/diagnostics16193238
UR - https://europepmc.org/article/MED/42882393
AB - Background/Objectives: Patient-based real-time quality control (PBRTQC) complements traditional internal quality control. This study validates a ratio-based PBRTQC integrating department clustering with inter-instrument ratio monitoring. Methods: Creatinine data (384,730 cases, 2025) from three measuring systems (A, B, C) were analyzed; conventional PBRTQC on unstratified Instrument A data served as comparator. Departments were stratified by K-means clustering of annual mean/SD, keeping the low-mean cluster (124 departments). The data of each measuring system were split chronologically: first-layer PBRTQC was trained on the first part, the second trained the ratio programs, and the third provided independent validation. Three systematic errors (fixed additive offsets of +5.28 and +10.56 μmol/L, and a progressive offset rising to +10.56 μmol/L, corresponding to 6% and 12% of the first-layer action limit) were simulated to assess the true positive rate (TPR), false positive rate (FPR), median samples to detection (MNPed), detection time and diagnostic odds ratio (DOR). Results: Under the fixed +5.28 μmol/L (6%) offset, A-B TPR was 28.83% versus 6.77% for traditional PBRTQC, with MNPed reduced from 34 to 20. Under the progressive offset rising to +10.56 μmol/L (12%), A-B TPR was 29.00% versus 8.35%, and detection time fell from 20 to 14 min. Under the fixed +10.56 μmol/L (12%) offset, A-B and A-C TPR reached 70.15% and 74.72% (MNPed 8 and 5; detection time 2 and 1 min). Across all scenarios the ratio programs achieved higher DOR. A stratified single-instrument comparator showed complementary contributions of stratification and ratio monitoring (TPR at the +5.28 μmol/L offset: 6.77%, 15.61% and 28.83%). Conclusions: Integrating department stratification with ratio-based PBRTQC markedly improves detection of small systematic errors in creatinine testing, albeit with more false positives, offering a transferable framework for laboratories using multiple same-platform measuring systems.
SN - 2075-4418
ER -