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Nguyen DD, Luo JW, Lu XH, Bechis SK, Sur RL, Nakada SY, Antonelli JA, Streeper NM, Sivalingam S, Viprakasit DP, Averch TD, Landman J, Chi T, Pais VM, Chew BH, Bird VG, Andonian S, Canvasser NE, Harper JD, Penniston KL, Bhojani N. 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). BJU Int 2020; 128:88-94. [PMID: 33205549 DOI: 10.1111/bju.15300] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Revised: 10/06/2020] [Accepted: 11/13/2020] [Indexed: 11/28/2022]
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.
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Affiliation(s)
- David-Dan Nguyen
- Faculty of Medicine, McGill University, Montreal, QC, Canada.,Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jack W Luo
- Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Xing Han Lu
- School of Computer Science, McGill University, Montreal, QC, Canada
| | - Seth K Bechis
- University of California San Diego School of Medicine, San Diego, CA, USA
| | - Roger L Sur
- University of California San Diego School of Medicine, San Diego, CA, USA
| | - Stephen Y Nakada
- Department of Urology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Jodi A Antonelli
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Sri Sivalingam
- Cleveland Clinic, Glickman Urological and Kidney Institute, Cleveland, OH, USA
| | | | | | - Jaime Landman
- University of California Irvine School of Medicine, Orange, CA, USA
| | - Thomas Chi
- University of California San Francisco School of Medicine, San Francisco, CA, USA
| | - Vernon M Pais
- Dartmouth Hitchcock Medical Center, Lebanon, NH, USA
| | - Ben H Chew
- Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada
| | - Vincent G Bird
- University of Florida College of Medicine, Gainesville, FL, USA
| | - Sero Andonian
- McGill University Health Center, Montreal, QC, Canada
| | - Noah E Canvasser
- University of California Davis School of Medicine, Sacramento, CA, USA
| | | | - Kristina L Penniston
- Department of Urology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Naeem Bhojani
- Division of Urology, Centre Hospitalier de l'Université de Montréal (CHUM), Université de Montréal, Montreal, QC, Canada
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