Abstract Objectives Kidney stones affect 12% of the population over their lifetime. Recurrent kidney stones lead to repeated interventions and excessive healthcare costs. Despite progress in imaging and metabolic evaluations, models to accurately identify patients at high risk are missing. In this study, we investigate whether machine learning methods can facilitate early identification of recurrent kidney stone formers. Patients and Methods This observational study included data from the single‐centric Bern Kidney Stone Registry. Each participant had at least one stone episode. Different data imputation techniques, such as kernel density estimation (KDE) imputation, median imputation and k ‐nearest neighbour (KNN) imputation, were evaluated in a logistic regression model. Feature selection with recursive feature elimination was applied. A fivefold cross‐validation was conducted using an 80/20 split. The classification criterion was recurrent kidney stone event. Results A total of 706 patients (median age, 47, 71.2% male) were included, and 563 (79.7%) had recurrent stone events. The median imputation yielded the best‐performing models. A mean receiver operating characteristic curve area under the curve (AUC) of 0.71 ± 0.03 was achieved on the held‐out test set. Estimated glomerular filtration rate (OR = 0.45, 95% CI: 0.42–0.49), age at first stone episode (OR = 0.50, 95% CI: 0.46–0.56), oxalate (OR = 1.83, 95% CI: 1.43–2.23) and pH (OR = 1.74, 95% CI: 1.47–1.89) were among the most descriptive features. Conclusion Routinely collected clinical and laboratory variables can be potentially exploited to identify recurrent stone formers, and our machine learning approach achieved better performance than previously reported work. With further validation on external datasets, our routine could support clinicians in designing dietary, medical or surveillance strategies, thereby reducing recurrence rates and improving long‐term outcomes for patients with stone‐forming conditions.
Amado et al. (Sun,) studied this question.