Improving the efficiency of quality control in clinical laboratory with an integrated PBRTQC system based on patient risk
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Xincen Duan
,
Tony Badrick
,
Wenqi Shao
,
Beili Wang
und
Wei Guo
Abstract
Objectives
Recent advances in information technology have renewed interest in patient-based real-time quality control (PBRTQC) as an alternative to internal quality control (IQC). However, since regulations mandate IQC, PBRTQC can only be implemented as a separate system. The additional labor required for PBRTQC may hinder widespread adoption. Therefore, a more efficient system that integrates IQC with PBRTQC is needed for laboratories to implement the methods effectively.
Methods
A QC system that integrates IQC with PBRTQC is proposed. The maximum average number of patients with unacceptable analytical errors (MaxANPTE) was introduced as a critical metric to benchmark the efficiency of the integrated PBRTQC system against the IQC-only system using a modified Parvin patient risk model. With the historical data of serum sodium (Na), chloride (Cl), alanine aminotransferase (ALT), and creatinine (CREA) from Zhongshan Hospital, Fudan University, in 2019, the integrated system incorporating the simple PBRTQC model and the more advanced regression-adjusted real-time quality control (RARTQC) were compared with the IQC-only system.
Results
In most cases, the integrated system incorporating RARTQC models outperformed the IQC-only system, particularly for ALT, where QC events were reduced by up to 45 %. Based on these findings, we proposed strategies for laboratories to design the integrated system.
Conclusions
The study demonstrated the improvement of efficiency of the integrated PBRTQC system over the IQC-only system. These insights can help laboratories make informed decisions on adopting PBRTQC models and provide as evidence for revising regulation on IQC.
Funding source: The Constructing Project of Clinical Key Disciplines Shanghai
Award Identifier / Grant number: shslczdzk03302
Funding source: National Key Research and Development Program of China
Award Identifier / Grant number: 2022YFC3602301
Funding source: the Xuhui District Artificial Intelligence Collaboration Project
Award Identifier / Grant number: 2021-001
Funding source: Zhongshan Hospital
Award Identifier / Grant number: 2023ZSQN30
Funding source: National Natural Science Foundation of China
Award Identifier / Grant number: 82102468
Award Identifier / Grant number: 82172348
Award Identifier / Grant number: 82402736
Award Identifier / Grant number: U23A20458
Acknowledgments
The study is supported by the National Natural Science Foundation of China [82402736, 82172348, 82102468, U23A20458]; the Xuhui District Artificial Intelligence Collaboration Project [2021-001]; Zhongshan Hospital [2023ZSQN30]; The Constructing Project of Clinical Key Disciplines Shanghai (shslczdzk03302); and the National Key Technologies R&D Program provided by the Ministry of Science and Technology of the People’s Republic of China [2022YFC3602301].
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Research ethics: The Medical Ethics Committee of Zhongshan Hospital approved using the extracted data and additional clinical information without the need for informed consent (approval number: B2020-392).
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: X.D. is supported by National Natural Science Foundation of China [82402736]; Zhongshan Hospital [2023ZSQN30]. W.G. is supported by National Natural Science Foundation of China [82172348, 82102468, U23A20458]; the Xuhui District Artificial Intelligence Collaboration Project [2021-001]; The Constructing Project of Clinical Key Disciplines Shanghai (shslczdzk03302); and the National Key Technologies R&D Program provided by the Ministry of Science and Technology of the People’s Republic of China [2022YFC3602301].
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Data availability: The code and resampled dataset are provided in the Supplement.
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Patents: The researches are involved in the process of applying several Chinese patents, CN116629668A, CN114330859A, CN110728315A.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/cclm-2025-0163).
© 2025 Walter de Gruyter GmbH, Berlin/Boston
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- Frontmatter
- Editorial
- Macroprolactinaemia – some progress but still an ongoing problem
- Review
- Understanding the circulating forms of cardiac troponin: insights for clinical practice
- Opinion Papers
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- IFCC recommendations for internal quality control practice: a missed opportunity
- Genetics and Molecular Diagnostics
- Evaluation of error detection and treatment recommendations in nucleic acid test reports using ChatGPT models
- General Clinical Chemistry and Laboratory Medicine
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