A Machine Learning Approach to Predict the Outcome of Urinary Calculi Treatment Using Shock Wave Lithotripsy: Model Development and Validation Study

Reihaneh Moghisi; Christo El Morr; Kenneth T Pace; Mohammad Hajiha; Jimmy Huang

doi:10.2196/33357

A Machine Learning Approach to Predict the Outcome of Urinary Calculi Treatment Using Shock Wave Lithotripsy: Model Development and Validation Study

Interact J Med Res. 2022 Mar 16;11(1):e33357. doi: 10.2196/33357.

Authors

Reihaneh Moghisi^#¹, Christo El Morr^#², Kenneth T Pace^{3

4}, Mohammad Hajiha⁵, Jimmy Huang¹

Affiliations

¹ School of Information Technology, York University, Toronto, ON, Canada.
² School of Health Policy and Management, York University, Toronto, ON, Canada.
³ Division of Urology, St Michael's Hospital, Toronto, ON, Canada.
⁴ Department of Surgery, University of Toronto, Toronto, ON, Canada.
⁵ Department of Urology, Loma Linda University Health, Loma Linda, CA, United States.

^# Contributed equally.

PMID: 35293872
PMCID: PMC8968550
DOI: 10.2196/33357

Abstract

Background: Shock wave lithotripsy (SWL), ureteroscopy, and percutaneous nephrolithotomy are established treatments for renal stones. Historically, SWL has been a predominant and commonly used procedure for treating upper tract renal stones smaller than 20 mm in diameter due to its noninvasive nature. However, the reported failure rate of SWL after one treatment session ranges from 30% to 89%. The failure rate can be reduced by identifying candidates likely to benefit from SWL and manage patients who are likely to fail SWL with other treatment modalities. This would enhance and optimize treatment results for SWL candidates.

Objective: We proposed to develop a machine learning model that can predict SWL outcomes to assist practitioners in the decision-making process when considering patients for stone treatment.

Methods: A data set including 58,349 SWL procedures performed during 31,569 patient visits for SWL to a single hospital between 1990 and 2016 was used to construct and validate the predictive model. The AdaBoost algorithm was applied to a data set with 17 predictive attributes related to patient demographics and stone characteristics, with success or failure as an outcome. The AdaBoost algorithm was also applied to a training data set. The generated model's performance was compared to that of 5 other machine learning algorithms, namely C4.5 decision tree, naïve Bayes, Bayesian network, K-nearest neighbors, and multilayer perceptron.

Results: The developed model was validated with a testing data set and performed significantly better than the models generated by the other 5 predictive algorithms. The sensitivity and specificity of the model were 0.875 and 0.653, respectively, while its positive predictive value was 0.7159 and negative predictive value was 0.839. The C-statistics of the receiver operating characteristic (ROC) analysis was 0.843, which reflects an excellent test.

Conclusions: We have developed a rigorous machine learning model to assist physicians and decision-makers to choose patients with renal stones who are most likely to have successful SWL treatment based on their demographics and stone characteristics. The proposed machine learning model can assist physicians and decision-makers in planning for SWL treatment and allow for more effective use of limited health care resources and improve patient prognoses.

Keywords: AdaBoost; ensemble learning; kidney disease; lithotripsy; machine learning; renal stones; treatment outcome; urolithiasis.

©Reihaneh Moghisi, Christo El Morr, Kenneth T Pace, Mohammad Hajiha, Jimmy Huang. Originally published in the Interactive Journal of Medical Research (https://www.i-jmr.org/), 16.03.2022.