Machine learning provides an accurate prognostication model for refractory overactive bladder treatment response and is noninferior to human experts

Objective: The increasing wealth of clinical data may become unmanageable for a physician to assimilate into optimal decision-making without assistance. Utilizing a novel machine learning (ML) approach, we sought to develop algorithms to predict patient outcomes following the overactive bladder treatments OnabotulinumtoxinA (OBTX-A) injection and sacral neuromodulation (SNM). Materials and Methods: ROSETTA datasets for overactive bladder patients randomized to OBTX-A or SNM were obtained. Novel ML algorithms, using reproducing kernel techniques were developed and tasked to predict outcomes including treatment response and decrease in urge urinary incontinence episodes in both the OBTX-A and SNM cohorts, in validation and test sets. Blinded expert urologists also predicted outcomes. Receiver operating characteristic curves were generated and AUCs calculated for comparison to lines of ignorance and the expert urologists’ predictions. Results: Trained algorithms demonstrated outstanding accuracy in predicting treatment response (OBTX-A: AUC 0.95; SNM: 0.88). Algorithms accurately predicted mean decrease in urge urinary incontinence episodes (MSE < 0.15) in OBTX-A and SNM. Algorithms were superior to human experts in response prediction for OBTX-A, and noninferior to human experts in response prediction for SNM. Conclusions: Novel ML algorithms were accurate, superior to expert urologists in predicting OBTX-A outcomes, and noninferior to expert urologists in predicting SNM outcomes. Some aspects of the physician–patient interaction are subtle and uncomputable, and thus ML may complement, but not supplant, a physician's judgment.