Machine learning models for screening clinically significant nephrolithiasis in overweight and obese populations
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/$ m^{2} $) 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..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:42 |
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| Enthalten in: |
World journal of urology - 42(2024), 1 vom: 09. März |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Chen, Hao-Wei [VerfasserIn] |
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| Links: |
Volltext [lizenzpflichtig] |
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| BKL: | |
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| Themen: |
Health services |
| Anmerkungen: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| doi: |
10.1007/s00345-024-04826-4 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a 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/$ m^{2} $) 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. | ||
| 650 | 4 | |a Nephrolithiasis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Overweight |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Obesity |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Wei, Pei-Siou |4 aut | |
| 700 | 1 | |a Chen, Yu-Chen |4 aut | |
| 700 | 1 | |a Wu, Jeng-Yih |4 aut | |
| 700 | 1 | |a Lin, Chia-I. |4 aut | |
| 700 | 1 | |a Chou, Yii-Her |4 aut | |
| 700 | 1 | |a Juan, Yung-Shun |4 aut | |
| 700 | 1 | |a Wu, Wen-Jeng |4 aut | |
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