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) 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.