Analysis of Factors Associated with Large Kidney Stones: Stone Composition, Comorbid Conditions, and 24-H Urine Parameters—a Machine Learning-Aided Approach

We aim to describe factors that are associated with kidney stones 20 mm or larger. This information would potentially guide research regarding factors of kidney stone growth. We retrospectively reviewed a patient cohort who underwent surgical treatment for kidney stones. Patients with detailed demographics, 24-h urine testing, and kidney stone profiling were included. Large stone was defined as measuring 20 mm or more. Univariate analysis was conducted to assess variables associated with kidney stones larger than 20 mm. Multivariate logistic regression and statistical machine learning methods were used to infer prediction models. The specific composition of kidney stones, laboratory testing results, and detailed demographics of 277 patients were included in our analysis. Multiple variables were significantly associated with large stones in univariate analysis. The final model that predicts large stone size includes several variables from different domains: hypertension (OR = 1.91; 95% CI 1.06, 3.43), older age (60+ vs 20–40) (OR = 2.46; 95% CI 1.07, 5.63), decreased calcium oxalate supersaturation (OR = 0.92; 95% CI 0.85, 0.99), and higher percentage of protein in stone composition (OR = 5.64; 95% CI 2.04, 15.58). This model yields a sensitivity 83% and specificity of 56%. Models using machine learning algorithms identified similar predictors, but the performance varies. Our model yielded good performance, and it could potentially be used as a clinical tool for predicting large stones and identifying factors affecting kidney stone growth. Similar analysis in other cohorts should be pursued to externally validate findings.