Abstract
Objectives
Patient-based real-time quality control (PBRTQC), a laboratory tool for monitoring the performance of the testing process, has gained increasing attention in recent years. It has been questioned for its generalizability among analytes, instruments, laboratories, and hospitals in real-world settings. Our purpose was to build a machine learning, nonlinear regression-adjusted, patient-based real-time quality control (mNL-PBRTQC) with wide application.
Methods
Using computer simulation, artificial biases were added to patient population data of 10 measurands. An mNL-PBRTQC was created using eight hospital laboratory databases as a training set and validated by three other hospitals’ independent patient datasets. Three different Patient-based models were compared on these datasets, the IFCC PBRTQC model, linear regression-adjusted real-time quality control (L-RARTQC), and the mNL-PBRTQC model.
Results
Our study showed that in the three independent test data sets, mNL-PBRTQC outperformed the IFCC PBRTQC and L-RARTQC for all measurands and all biases. Using platelets as an example, it was found that for 20 % bias, both positive and negative, the uncertainty of error detection for mNL-PBRTQC was smallest at the median and maximum values.
Conclusions
mNL-PBRTQC is a robust machine learning framework, allowing accurate error detection, especially for analytes that demonstrate instability and for detecting small biases.
Funding source: Excellence project of key clinical specialty in Beijing
Funding source: Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support
Award Identifier / Grant number: ZYLX201811
Funding source: National Natural Science Foundation of China
Award Identifier / Grant number: 72374145
Acknowledgments
We thank all those who participated in this study.
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Research ethics: The project was approved by the local hospital Ethics Committee.
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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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Competing interests: Authors state no conflict of interest.
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Research funding: This work was supported by Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support (ZYLX201811) , Excellence project of key clinical specialty in Beijing and National Natural Science Foundation of China (72374145).
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Data availability: The data are not publicly available.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/cclm-2023-0964).
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