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)
| Autor: | David-Dan Nguyen, Stephen Y. Nakada, Thomas Chi, Naeem Bhojani, Necole M. Streeper, Kristina L. Penniston, Roger L. Sur, Jaime Landman, Jodi Antonelli, Jonathan D. Harper, Timothy D. Averch, Seth K. Bechis, Noah Canvasser, Jack W. Luo, Davis P. Viprakasit, Sri Sivalingam, Sero Andonian, Ben H. Chew, Vincent G. Bird, Vernon M. Pais, Xing Han Lu |
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| Rok vydání: | 2020 |
| Předmět: |
Adult
Male Multivariate statistics Urology 030232 urology & nephrology MEDLINE Machine Learning Correlation Kidney Calculi 03 medical and health sciences 0302 clinical medicine Quality of life medicine Humans Aged business.industry Middle Aged medicine.disease Regression 030220 oncology & carcinogenesis Quality of Life Female Kidney stones Self Report Gradient boosting business Algorithm Body mass index |
| Zdroj: | BJU International. 128:88-94 |
| ISSN: | 1464-410X 1464-4096 |
| DOI: | 10.1111/bju.15300 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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