Authors : Mahendra Bhandari, Anubhav Reddy Nallabasannagari,Madhu Reddiboina , James R. Porter,Wooju Jeong, Alexandre Mottrie, Prokar Dasgupta, Ben Challacombe, Ronney Abaza,Koon Ho Rha,Dipen J. Parekh,Rajesh Ahlawat, Umberto Capitanio, Thyavihally B. Yuvaraja , Sudhir Rawal, Daniel A. Moon,Nicolò M. Buffi, Ananthakrishnan Sivaraman , Kris K. Maes, Francesco Porpiglia, Gagan Gautam, Levent Turkeri , Kohul Raj Meyyazhgan, Preethi Patil , Mani Menon, Craig Rogers
Procedure Followed : RAPN / Machine learning
Institutions : VCQI / BJUI
Abstract : The Vattikuti Collective Quality Initiative database was used for a study of Machine Learning (ML) being used to help predict outcomes for partial nephrectomy patients. 'Predicting intra‐operative and postoperative consequential events using machine‐learning techniques in patients undergoing robot‐assisted partial nephrectomy: a Vattikuti Collective Quality Initiative database study' has been published in the April 21 issue of the BJUI Journal. Objective : To predict intra‐operative (IOEs) and postoperative events (POEs) consequential to the derailment of the ideal clinical course of patient recovery. Materials and Methods : The Vattikuti Collective Quality Initiative is a multi‐institutional dataset of patients who underwent robot‐assisted partial nephectomy for kidney tumours. Machine‐learning (ML) models were constructed to predict IOEs and POEs using logistic regression, random forest and neural networks. The models to predict IOEs used patient demographics and preoperative data. In addition to these, intra‐operative data were used to predict POEs. Performance on the test dataset was assessed using area under the receiver‐operating characteristic curve (AUC‐ROC) and area under the precision‐recall curve (PR‐AUC).