To validate the output of a machine learning-based software as an intelligible interface for predicting multiple outcomes after percutaneous nephrolithotomy (PCNL). We compared the performance of this system with Guy's stone score (GSS) and the Clinical Research Office of Endourological Society (CROES) nomogram.
Data from 146 adult patients (87 males, 59%) who underwent PCNL at our institute were used. To validate the system, accuracy of the software for predicting each postoperative outcome was compared to the actual outcome. Similarly, preoperative data were analyzed with GSS and CROES nomograms to determine stone-free status as predicted by these nomograms. A receiver operating characteristic (ROC) curve was generated for each scoring system, and the area under curve (AUC) was calculated and used to assess the predictive performance of all three models.
Overall stone-free rate (SFR) was 72.6% (106/146). Forty of 146 patients (27.4%) were scheduled for Forty two ancillary procedures (shock wave lithotripsy (SWL) (n= 31) or repeat PCNL (n=11)) to manage residual renal stones. Overall, the machine learning system predicted the PCNL outcomes with an accuracy ranging between 80% and 95.1%. For predicting the stone-free status, the area under the ROC curve for the software (0.915) was significantly larger than the AUC for GSS (0.615) or CROES nomograms (0.621) (p<0.001).
At the internal institutional level, the machine learning-based software was a promising tool for recording, processing and predicting outcomes after PCNL. Validation of this system against an external dataset is highly recommended before its widespread application.
Journal of endourology. 2019 Dec 28 [Epub ahead of print]
Alireza Aminsharifi, Daryush Irani, Sona Tayebi, Taher Jafari Kafash, Tayebeh Shabanian, Hossein Parsaei
Cleveland Clinic Foundation, 2569, Urology, CLEVELAND CLINIC, CLEVELAND, Cleveland, Ohio, United States, 44195-5243., ShIRAZ UNIVERSITY OF MEDICAL SCIENCES, UROLOGY, Shahid faghihi hospital, Shiraz, Fars, Iran, Islamic Republic of, P.O. 71345-1636., Shiraz, Iran (the Islamic Republic of); ., Shiraz, Iran (the Islamic Republic of); ., Shiraz, Iran (the Islamic Republic of); ., Shiraz, Iran (the Islamic Republic of); .