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

Abstract

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.

Artificial intelligence in endourology: emerging technology for individualized care

“…Chen et al evaluated several machine learning algorithms to predict stone burden at least 2 cm (Table 1). Performance for all algorithms was modest (optimal specificity 72% and positive predictive value 59%) and inferior to a traditional logistic regression model [6].…”

Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review

“…[ 59 ] Risk factors for calcium stones Case-control CaOx supersaturation and 24 h-urea for all men and women with a family history No comparator Kazemi and Mirroshandel [ 60 ] Risk of nephrolithiasis Cohort Accuracy of 97.1% Other classifiers with lower accuracy Chen et al. [ 61 ] Risk of forming renal stones of >2 cm Cohort AUC of 0.69 AUC of 0.74 Kavoussi et al. [ 62 ] Prediction of 24 h urine abnormalities relevant for stone disease Cohort Higher accuracy in prediction of urine volume, uric acid, and natrium abnormalities Higher accuracy in prediction of pH and citrate abnormalities Caudarella et al.…”

“…Ensemble learning was applied in a recent study to extract the relevant features across a data set with 42 clinical and biochemical features and to construct a predicting model for developing stone disease with an accuracy of 97.1% [ 60 ]. Especially for kidney stones larger than 20 mm, another model based on ML algorithms defined hypertension, older age, decreased CaOx supersaturation, and a higher percentage of protein in stone composition as the strongest predictors for developing kidney stones of the above size category [ 61 ]. Importantly, in this report, AI-based prediction was adequate but so accurate as the result of LR.…”

The Ascent of Artificial Intelligence in Endourology: a Systematic Review Over the Last 2 Decades

“…Chen et al used ML methods to study the risk factors (hypertension, increased protein content in stones, decreased calcium oxalate supersaturation, and old age) causing renal stones > 20 mm using demographic variables, 24-h urine profile, and stone profile data of 277 patients. This model yields sensitivity and specificity of 83% and 56% respectively [59].…”