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Author: search.filters.author.Ahlawat, RajeshSubject: search.filters.subject.postoperative complications

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    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
    (WILEY, 2020-01-01) Bhandari, Mahendra; Nallabasannagari, Anubhav Reddy; Reddiboina, Madhu; Porter, James R.; Jeong, Wooju; Mottrie, Alexandre; Dasgupta, Prokar; Challacombe, Ben; Abaza, Ronney; Rha, Koon Ho; Parekh, Dipen J.; Ahlawat, Rajesh; Capitanio, Umberto; Yuvaraja, Thyavihally B.; Rawal, Sudhir; Moon, Daniel A.; Buffi, Nicolo M.; Sivaraman, Ananthakrishnan; Maes, Kris K.; Porpiglia, Francesco; Gautam, Gagan; Turkeri, Levent; Meyyazhgan, Kohul Raj; Patil, Preethi; Menon, Mani; Rogers, Craig
    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