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Objective: To assess the impact of kidney stone disease (KSD) and its treatment on the health‐related quality of life (HRQOL) of high‐risk stone formers with hyperparathyroidism, renal tubular acidosis, malabsorptive disease, and medullary sponge kidney. Patients and Methods: The Wisconsin Stone Quality of Life questionnaire was used to evaluate HRQOL in 3301 patients with a history of KSD from 16 institutions in North America between 2014 and 2020. Baseline characteristics and medical history were collected from patients, while active KSD was confirmed through radiological imaging. The high‐risk group was compared to the remaining patients (control group) using the Wilcoxon rank‐sum test. Results: Of 1499 patients with active KSD included in the study, the high‐risk group included 120 patients. The high‐risk group had significantly lower HRQOL scores compared to the control group (P < 0.01). In the multivariable analyses, medullary sponge kidney disease and renal tubular acidosis were independent predictors of poorer HRQOL, while alkali therapy was an independent predictor of better HRQOL (all P < 0.01). Conclusions: Among patients with active KSD, high‐risk stone formers had impaired HRQOL with medullary sponge kidney disease and renal tubular acidosis being independent predictors of poorer HRQOL. Clinicians should seek to identify these patients earlier as they would benefit from prompt treatment and prevention. [ABSTRACT FROM AUTHOR]

Objective: To build the Wisconsin Stone Quality of Life Machine‐Learning Algorithm (WISQOL‐MLA) to predict urolithiasis patients' health‐related quality of life (HRQoL) based on demographic, symptomatic and clinical data collected for the validation of the Wisconsin Stone Quality‐of‐Life (WISQOL) questionnaire, an HRQoL measurement tool designed specifically for patients with kidney stones. Material and Methods: We used data from 3206 stone patients from 16 centres. We used gradient‐boosting and deep‐learning models to predict HRQoL scores. We also stratified HRQoL scores by quintile. The dataset was split using a standard 70%/10%/20% training/validation/testing ratio. Regression performance was evaluated using Pearson's correlation. Classification was evaluated with an area under the receiver‐operating characteristic curve (AUROC). Results: Gradient boosting obtained a test correlation of 0.62. Deep learning obtained a correlation of 0.59. Multivariate regression achieved a correlation of 0.44. Quintile stratification of all patients in the WISQOL dataset obtained an average test AUROC of 0.70 for the five classes. The model performed best in identifying the lowest (0.79) and highest quintiles (0.83) of HRQoL. Feature importance analysis showed that the model weighs in clinically relevant factors to estimate HRQoL, such as symptomatic status, body mass index and age. Conclusions: Harnessing the power of the WISQOL questionnaire, our initial results indicate that the WISQOL‐MLA can adequately predict a stone patient's HRQoL from readily available clinical information. The algorithm adequately relies on relevant clinical factors to make its HRQoL predictions. Future improvements to the model are needed for direct clinical applications. [ABSTRACT FROM AUTHOR]