Investigating surgeon performance metrics as key predictors of robotic herniorrhaphy outcomes using iterative machine learning models: retrospective study.

Abstract

Background: Robotic data streams allow for capture of objective performance indicators, providing the ability to quantify and analyse operator technique and movement in optimizing postoperative outcomes. This study provided proof-of-concept demonstration of how intraoperative surgeon-factors could influence post-robotic herniorrhaphy complications via machine learning analyses of objective performance indicators.

Study design: Data on robotic-assisted ventral hernia repair were retrospectively reviewed between February 2013 and November 2022 at a single academic centre. Machine learning modelling on systematic chart review data correlated perioperative patient factors, intraoperative objective performance indicators, and postoperative outcomes. Complications were classified with the Clavien-Dindo scale. Endpoints of interest included postoperative complications at discharge, at postoperative day 30, and at the last follow-up. Machine learning models employed included linear, k-nearest neighbours, support vector, decision tree, random forest, adaptive boosting, and extreme gradient boosting regression algorithms.

Results: Some 520 patients undergoing robotic ventral hernia were included. Median age of patients was 56 years with 52.7% male and median body mass index 31.9 kg/m2. 92.7% of patients had at least one medical comorbidity peoperatively. Complications occurred in 33 (6.3%) patients at time of discharge. Machine learning models demonstrated an accuracy 0.95, a precision 0.92, a recall 0.95, and a F1 0.92 of objective performance indicator predicting complications and an accuracy 0.95, a precision 0.95, a recall 0.95, and a F1 0.94 by Clavien-Dindo grade at time of discharge. Thematic analyses of top ranked factors included operator-specific objective performance indicators alongside patient factors canonically associated with hernia complications.

Conclusions: This study showed the novel application of machine learning modelling to bridge objective performance indicators and clinical patient factors to postoperative clinical outcomes, demonstrating the relevance of dynamic intraoperative surgeon factors on clinical outcomes.