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 Bhandari¹, Anubhav Reddy Nallabasannagari², Madhu Reddiboina², James R Porter³, Wooju Jeong¹, Alexandre Mottrie⁴, Prokar Dasgupta⁵, Ben Challacombe⁶, Ronney Abaza⁷, Koon Ho Rha⁸, Dipen J Parekh⁹, Rajesh Ahlawat¹⁰, Umberto Capitanio¹¹, Thyavihally B Yuvaraja¹², Sudhir Rawal¹³, Daniel A Moon¹⁴, Nicolò M Buffi¹⁵, Ananthakrishnan Sivaraman¹⁶, Kris K Maes¹⁷, Francesco Porpiglia¹⁸, Gagan Gautam¹⁹, Levent Turkeri²⁰, Kohul Raj Meyyazhgan², Preethi Patil²¹, Mani Menon¹, Craig Rogers¹

¹Vattikuti Urology Institute, Henry Ford Hospital, Detroit, MI, USA.

²RediMinds Inc., Southfield, MI, USA.

³Swedish Medical Centre, Seattle, WA, USA.

⁴OLV Vattikuti Institute, Aalst, Belgium.

⁵MRC Centre of Transplantation, King's College London, London, UK.

⁶Guy's and St Thomas' Hospitals, London, UK.

⁷Ohio Health Dublin Methodist Hospital, Dublin, OH, USA.

⁸Yonsei University, Seoul, Korea.

⁹Sylvester Comprehensive Cancer Centre, University of Miami Miller School of Medicine, Miami, FL, USA.

¹⁰Medanta Vattikuti Institute, Medanta - The Medicity, Gurugram, Haryana, India.

¹¹Urology Clinic, San Raffaele Hospital, Milan, Italy.

¹²Kokilaben Dhirubhai Ambani Hospital, Mumbai, India.

¹³Rajiv Gandhi Cancer Institute and Research Centre, New Delhi, India.

¹⁴Peter MacCallum Cancer Centre, Melbourne, Vic., Australia.

¹⁵Humanitas Research Hospital, Milan, Italy.

¹⁶Apollo Hospitals, Chennai, India.

¹⁷Centre for Robotic and Minimally Invasive Surgery, Hospital Da Luz, Luz Sáude, Portugal.

¹⁸San Luigi Gonzaga Hospital, University of Turin, Orbassano, Italy.

¹⁹Max Institute of Cancer Care, Saket, India.

²⁰Acıbadem University School of Medicine, Istanbul, Turkey.

²¹Vattikuti Foundation, Southfield, MI, USA.

PubMed DOI

Abstract

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 IOE…

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Mentioned Companies

R
RediMinds
affiliation

Other Funding

RediMinds Inc.
International

MeSH Terms

Aged0Databases, Factual0Female0Humans0Intraoperative Complications0Kidney Neoplasms0Machine Learning*Male0Middle Aged0Nephrectomy0Postoperative Complications0Prognosis0Retrospective Studies0Robotic Surgical Procedures*

Keywords

deep learningintra-operative complicationsmachine learningpostoperative complicationspostoperative morbidityrobot-assisted partial nephrectomy

Article Info

PMID: 32315504
DOI: 10.1111/bju.15087
Accepted: April 15, 2020

How to Cite

Bhandari M et al. 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. BJU Int2020;126(3):350-358. doi:10.1111/bju.15087

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