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). (7th December 2020)

• Record Type: Journal Article
• Authors: Nguyen, David‐Dan; Luo, Jack W.; Lu, Xing Han; Bechis, Seth K.; Sur, Roger L.; Nakada, Stephen Y.; Antonelli, Jodi A.; Streeper, Necole M.; Sivalingam, Sri; Viprakasit, Davis P.; Averch, Timothy D.; Landman, Jaime; Chi, Thomas; Pais, Vernon M.; Chew, Ben H.; Bird, Vincent G.; Andonian, Sero; Canvasser, Noah E.; Harper, Jonathan D.; Penniston, Kristina L.; Bhojani, Naeem
• Is Part Of: BJU international. Volume 128:Number 1(2021)
• Page Start: 88
• Page End: 94
• Publication Date: 2020-12-07
• Subjects: nephrolithiasis -- kidney stones -- health‐related quality of life -- WISQOL -- machine learning -- #KidneyStones -- #UroStone; Genitourinary organs -- Diseases -- Periodicals; Genitourinary organs -- Surgery -- Periodicals; Urology -- Periodicals; 616.6
• DOI: 10.1111/bju.15300
• Languages: English
• ISSNs: 1464-4096

Abstract

Abstract : 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.