JMIR medical informatics · 2026
Screening for Clinically Significant Nephrolithiasis Based on Simple Health Checkup Clinical and Urine Parameters in General Populations: Multicenter Machine Learning Study.
СтатьяМногоцентровое исследованиеНаблюдательное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Chen Hao-Wei, Wei Pei-Siou, Chen Yu-Chen, Wu Jeng-Yih, Lin Chia-I, Chou Yii-Her, Juan Yung-Shun, Wu Wen-Jeng, Kao Chung-Yao, Lee Jung-Ting
Аннотация
BACKGROUND: Nephrolithiasis affects approximately 15% of the population and often remains undetected in asymptomatic individuals. Current diagnostic approaches rely on imaging tools, such as ultrasound or computed tomography, which are costly, operator dependent, or involve radiation, making them unsuitable for large-scale screening. A standardized, practical, and low-cost screening strategy for early identification of clinically significant kidney stones is still lacking.
OBJECTIVE: This study aimed to develop a low-cost, rapid screening model for clinically significant nephrolithiasis using machine learning (ML) and simple clinical parameters.
METHODS: We conducted a multihospital retrospective study using data from 3 hospitals in Kaohsiung, Taiwan (2012-2021). Adults without renal colic were included. ML models were trained and tested using 10 routine variables: sex, age, BMI, gout, diabetes, estimated glomerular filtration rate, urine pH, red blood cell count, specific gravity, and bacteriuria. Multiple ML algorithms were trained and evaluated, and the best-performing model was selected based on the area under the receiver operating characteristic curve and the area under the precision-recall curve. To assess model interpretability, Shapley value analysis was performed to determine the relative importance and contribution of each variable to the model's predictive performance.
RESULTS: Among 6528 participants, the best-performing model achieved an area under the receiver operating characteristic curve of 0.968 (95% CI 0.956-0.980), an area under the precision-recall curve of 0.936 (95% CI 0.918-0.953), a sensitivity of 0.873 (95% CI 0.841-0.904), and a specificity of 0.947 (95% CI 0.935-0.959). Shapley value analysis identified urine red blood cell count, estimated glomerular filtration rate, and urine specific gravity as the 3 most influential predictors.
CONCLUSIONS: This ML-based model enables efficient, noninvasive, and large-scale kidney stone screening using routine health data. It can be integrated into health checkups or telemedicine platforms to facilitate early detection and proactive management. Although the model was developed using an Asian population, future validation in diverse cohorts is warranted to confirm its generalizability.
OBJECTIVE: This study aimed to develop a low-cost, rapid screening model for clinically significant nephrolithiasis using machine learning (ML) and simple clinical parameters.
METHODS: We conducted a multihospital retrospective study using data from 3 hospitals in Kaohsiung, Taiwan (2012-2021). Adults without renal colic were included. ML models were trained and tested using 10 routine variables: sex, age, BMI, gout, diabetes, estimated glomerular filtration rate, urine pH, red blood cell count, specific gravity, and bacteriuria. Multiple ML algorithms were trained and evaluated, and the best-performing model was selected based on the area under the receiver operating characteristic curve and the area under the precision-recall curve. To assess model interpretability, Shapley value analysis was performed to determine the relative importance and contribution of each variable to the model's predictive performance.
RESULTS: Among 6528 participants, the best-performing model achieved an area under the receiver operating characteristic curve of 0.968 (95% CI 0.956-0.980), an area under the precision-recall curve of 0.936 (95% CI 0.918-0.953), a sensitivity of 0.873 (95% CI 0.841-0.904), and a specificity of 0.947 (95% CI 0.935-0.959). Shapley value analysis identified urine red blood cell count, estimated glomerular filtration rate, and urine specific gravity as the 3 most influential predictors.
CONCLUSIONS: This ML-based model enables efficient, noninvasive, and large-scale kidney stone screening using routine health data. It can be integrated into health checkups or telemedicine platforms to facilitate early detection and proactive management. Although the model was developed using an Asian population, future validation in diverse cohorts is warranted to confirm its generalizability.