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Machine learning models for screening clinically significant nephrolithiasis in overweight and obese populations

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Abstract

Purposes

Our aim is to build and evaluate models to screen for clinically significant nephrolithiasis in overweight and obesity populations using machine learning (ML) methodologies and simple health checkup clinical and urine parameters easily obtained in clinics.

Methods

We developed ML models to screen for clinically significant nephrolithiasis (kidney stone > 2 mm) in overweight and obese populations (body mass index, BMI ≥ 25 kg/m2) using gender, age, BMI, gout, diabetes mellitus, estimated glomerular filtration rate, bacteriuria, urine pH, urine red blood cell counts, and urine specific gravity. The data were collected from hospitals in Kaohsiung, Taiwan between 2012 and 2021.

Results

Of the 2928 subjects we enrolled, 1148 (39.21%) had clinically significant nephrolithiasis and 1780 (60.79%) did not. The testing dataset consisted of data collected from 574 subjects, 235 (40.94%) with clinically significant nephrolithiasis and 339 (59.06%) without. One model had a testing area under curve of 0.965 (95% CI, 0.9506–0.9794), a sensitivity of 0.860 (95% CI, 0.8152–0.9040), a specificity of 0.947 (95% CI, 0.9230–0.9708), a positive predictive value of 0.918 (95% CI, 0.8820–0.9544), and negative predictive value of 0.907 (95% CI, 0.8756–0.9371).

Conclusion

This ML-based model was found able to effectively distinguish the overweight and obese subjects with clinically significant nephrolithiasis from those without. We believe that such a model can serve as an easily accessible and reliable screening tool for nephrolithiasis in overweight and obesity populations and make possible early intervention such as lifestyle modifications and medication for prevention stone complications.

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Data availability

The datasets generated and/or analyzed during the current study are available from the first author or the corresponding author upon reasonable request.

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Acknowledgments

This work was supported in part by the National Science and Technology Council of Taiwan (NSTC 111-2221-E-037-008, NSTC 111-2320-B-110-002), in part by the NSYSU-KMU joint research project (#NSYSUKMU 111-P09), and Kaohsiung Municipal Ta-Tung Hospital, Taiwan (kmtth-111-R009). The authors declare that there are no other conflicts of interest.

Author information

Authors and Affiliations

  1. Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, 100, Shih-Chuan 1st Road, Kaohsiung, Taiwan

    Hao-Wei Chen, Yu-Chen Chen, Yii-Her Chou, Yung-Shun Juan & Wen-Jeng Wu

  2. Department of Urology, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung, Taiwan

    Hao-Wei Chen

  3. Department of Urology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan

    Hao-Wei Chen, Yu-Chen Chen, Yii-Her Chou, Yung-Shun Juan & Wen-Jeng Wu

  4. School of Medicine, National Sun Yat-Sen University, Kaohsiung, Taiwan

    Jung-Ting Lee

  5. Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan

    Pei-Siou Wei & Chung-Yao Kao

  6. Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan

    Jeng-Yih Wu

  7. Department of Health Management Center, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan

    Jeng-Yih Wu

  8. Faculty of College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan

    Jeng-Yih Wu

  9. Health Management Center, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung, Taiwan

    Chia-I. Lin

Authors
  1. Hao-Wei Chen
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  2. Jung-Ting Lee
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  3. Pei-Siou Wei
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  4. Yu-Chen Chen
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  7. Yii-Her Chou
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  9. Wen-Jeng Wu
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  10. Chung-Yao Kao
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Contributions

HW Chen: Project development, Data Collection, Data analysis, Manuscript writing/editing. JT Lee: Project development, Data analysis, Manuscript writing/editing. PS Wei: Data Management, Data analysis, Manuscript editing. YC Chen: Data Collection, Manuscript editing. JY Wu: Data Collection. CI Lin: Data Collection. YH Chou: Data Collection. YS Juan: Data Collection. WJ Wu: Data Collection, Manuscript editing. CY Kao: Project development, Data Management, Data analysis, Manuscript writing/editing.

Corresponding author

Correspondence to Chung-Yao Kao.

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Conflict of interest

The authors declare that there is no conflicts of interest.

Ethical standards

This is a retrospective cohort study based on past medical data; no human participants or animals were involved. The protocol for study was approved by the IRB at Kaohsiung Medical University Hospital (KMUHIRB-E(I)-20210331). The study was conducted following the principles set forth in the Declaration of Helsinki. The requirement for informed consent was waived because the study was retrospective by design and posed no risk to the subjects.

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Chen, HW., Lee, JT., Wei, PS. et al. Machine learning models for screening clinically significant nephrolithiasis in overweight and obese populations. World J Urol 42, 128 (2024). https://doi.org/10.1007/s00345-024-04826-4

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  • DOI: https://doi.org/10.1007/s00345-024-04826-4

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