Abstract
Objectives
Early recognition and timely intervention for urosepsis are key to reducing morbidity and mortality. Blood culture has low sensitivity, and a long turnaround time makes meeting the needs of clinical diagnosis difficult. This study aimed to use biomarkers to build a machine learning model for early prediction of urosepsis.
Methods
Through retrospective analysis, we screened 157 patients with urosepsis and 417 patients with urinary tract infection. Laboratory data of the study participants were collected, including data on biomarkers, such as procalcitonin, D-dimer, and C-reactive protein. We split the data into training (80%) and validation datasets (20%) and determined the average model prediction accuracy through cross-validation.
Results
In total, 26 variables were initially screened and 18 were statistically significant. The influence of the 18 variables was sorted using three ranking methods to further determine the best combination of variables. The Gini importance ranking method was found to be suitable for variable filtering. The accuracy rates of the six machine learning models in predicting urosepsis were all higher than 80%, and the performance of the artificial neural network (ANN) was the best among all. When the ANN included the eight biomarkers with the highest influence ranking, its model had the best prediction performance, with an accuracy rate of 92.9% and an area under the receiver operating characteristic curve of 0.946.
Conclusions
Urosepsis can be predicted using only the top eight biomarkers determined by the ranking method. This data-driven predictive model will enable clinicians to make quick and accurate diagnoses.
Funding source: Natural Science Foundation of Fujian Province
Award Identifier / Grant number: 2021J011447
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Research funding: This study was funded by the Natural Science Foundation of Fujian Province (grant number 2021J011447).
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Author’s contributions: JG wrote the manuscript. JH and HC collected the clinical data and performed data analysis. MS contributed to the conception, design, and code writing of this study. 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 conflicts of interest.
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Informed Consent: The requirement for informed consent was waived for all individuals included in this study.
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Ethical Approval: Research involving human subjects complied with all relevant national regulations and institutional policies and is in accordance with the tenets of the Helsinki Declaration (as revised in 2013) and has been approved by the Ethics Committee of Mindong Hospital affiliated with Fujian Medical University (0325-17).
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Supplementary Material
The online version of this article offers supplementary material (https://doi.org/10.1515/cclm-2022-1006).
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