Development and use of machine learning models for prediction of male sling success
A proof-of-concept institutional evaluation
DOI:
https://doi.org/10.5489/cuaj.8265Keywords:
Machine learning, prediction, male sling, transobturatorAbstract
INTRODUCTION: For mild to moderate male stress urinary incontinence (SUI), transobturator male slings remain an effective option for management. We aimed to use a machine learning (ML)-based model to predict those who will have a long-term success in managing SUI with male sling.
METHODS: All transobturator male sling cases from August 2006 to June 2012 by a single surgeon were reviewed. Outcome of interest was defined as ‘cure’: complete dryness with 0 pads used, without the need for additional procedures. Clinical variables included in ML models were: number of pads used daily, age, height, weight, race, incontinence type, etiology of incontinence, history of radiation, smoking, bladder neck contracture, and prostatectomy. Model performance was assessed using area under receiver operating characteristic curve (AUROC), area under precision-recall curve (AUPRC), and F1-score.
RESULTS: A total of 181 patients were included in the model. The mean followup was 56.4 months (standard deviation [SD] 41.6). Slightly more than half (53.6%, 97/181) of patients had procedural success. Logistic regression, K-nearest neighbor (KNN), naive Bayes, decision tree, and random forest models were developed using ML. KNN model had the best performance, with AUROC of 0.759, AUPRC of 0.916, and F1-score of 0.833. Following ensemble learning with bagging and calibration, KNN model was further improved, with AUROC of 0.821, AUPRC of 0.921, and F-1 score of 0.848.
CONCLUSIONS: ML-based prediction of long-term transobturator male sling is feasible. The low numbers of patients used to develop the model prompt further validation and development of the model but may serve as a decision-making aid for practitioners in the future.
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