King's College London

Research portal

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

Research output: Contribution to journalArticle

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 & 6 more Gagan Gautam, Levent Turkeri, Kohul Raj Meyyazhgan, Preethi Patil, Mani Menon, Craig Rogers

Original languageEnglish
Pages (from-to)350-358
Number of pages9
JournalBJU International
Issue number3
Publication statusPublished - 1 Sep 2020

King's Authors


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). Results: The rates of IOEs and POEs were 5.62% and 20.98%, respectively. Models for predicting IOEs were constructed using data from 1690 patients and 38 variables; the best model had an AUC-ROC of 0.858 (95% confidence interval [CI] 0.762, 0.936) and a PR-AUC of 0.590 (95% CI 0.400, 0.759). Models for predicting POEs were trained using data from 1406 patients and 59 variables; the best model had an AUC-ROC of 0.875 (95% CI 0.834, 0.913) and a PR-AUC 0.706 (95% CI, 0.610, 0.790). Conclusions: The performance of the ML models in the present study was encouraging. Further validation in a multi-institutional clinical setting with larger datasets would be necessary to establish their clinical value. ML models can be used to predict significant events during and after surgery with good accuracy, paving the way for application in clinical practice to predict and intervene at an opportune time to avert complications and improve patient outcomes.

View graph of relations

© 2018 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454