Journal article

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

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, Nicolo M Buffi, Ananthakrishnan Sivaraman, Kris K Maes, Francesco Porpiglia Show all

BJU INTERNATIONAL | WILEY | Published : 2020


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-operati..

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Funding Acknowledgements

We gratefully acknowledge discussions and comments on the manuscript by our colleague Trevor Zeffiro. We are grateful to the Vattikuti Foundation for granting access to the VCQI database and RediMinds for funding this work. This publication only reflects the authors views. The funding agency is not liable for any use that may be made of the information contained herein.