Estimating the Health-Related Quality of Life of Kidney Stone Patients: Initial Results from the Wisconsin Stone Quality of Life Machine Learning Algorithm (WISQOL-MLA).

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Abstract

To build the Wisconsin 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 WISQOL, an HRQOL measurement tool designed specifically for kidney stone patients.
We used data from 3206 stone patients from 16 centers. We used gradient boosting and deep learning models to predict HRQOL scores. We also stratified HRQOL scores by quintiles. 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 (AUROC) curve.
Gradient boosting obtained a test correlation of 0.62. Deep learning obtained a correlation of 0.59. Multivariate regression only achieved a correlation of 0.44. Quintile stratification on all WISQOL patients obtained an average test AUROC of 0.70 for the 5 classes. The model performed best in identifying lowest (0.79) and highest quintile (0.83) of HRQOL. Feature importance analysis showed that the model weighs in clinically relevant factors such as symptomatic status, body mass index, and age to estimate HRQOL.
Harnessing the power of the WISQOL questionnaire, our initial results indicate that 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.
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