Surgical gestures as a method to quantify surgical performance and predict patient outcomes

• Published in: NPJ digital medicine Vol. 5; no. 1; pp. 187 - 8
• Main Authors: Ma, Runzhuo, Ramaswamy, Ashwin, Xu, Jiashu, Trinh, Loc, Kiyasseh, Dani, Chu, Timothy N., Wong, Elyssa Y., Lee, Ryan S., Rodriguez, Ivan, DeMeo, Gina, Desai, Aditya, Otiato, Maxwell X., Roberts, Sidney I., Nguyen, Jessica H., Laca, Jasper, Liu, Yan, Urbanova, Katarina, Wagner, Christian, Anandkumar, Animashree, Hu, Jim C., Hung, Andrew J.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 22.12.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 692/308/409; 692/700/228; Biomedicine; Biotechnology; Clinical outcomes; Medicine; Medicine & Public Health; Quality of care; Skills; Surgeons; Surgery; Surgical outcomes; Surgical techniques; Urological surgery
• ISSN: 2398-6352; 2398-6352
• DOI: 10.1038/s41746-022-00738-y

How well a surgery is performed impacts a patient’s outcomes; however, objective quantification of performance remains an unsolved challenge. Deconstructing a procedure into discrete instrument-tissue “gestures” is a emerging way to understand surgery. To establish this paradigm in a procedure where performance is the most important factor for patient outcomes, we identify 34,323 individual gestures performed in 80 nerve-sparing robot-assisted radical prostatectomies from two international medical centers. Gestures are classified into nine distinct dissection gestures (e.g., hot cut) and four supporting gestures (e.g., retraction). Our primary outcome is to identify factors impacting a patient’s 1-year erectile function (EF) recovery after radical prostatectomy. We find that less use of hot cut and more use of peel/push are statistically associated with better chance of 1-year EF recovery. Our results also show interactions between surgeon experience and gesture types—similar gesture selection resulted in different EF recovery rates dependent on surgeon experience. To further validate this framework, two teams independently constructe distinct machine learning models using gesture sequences vs. traditional clinical features to predict 1-year EF. In both models, gesture sequences are able to better predict 1-year EF (Team 1: AUC 0.77, 95% CI 0.73–0.81; Team 2: AUC 0.68, 95% CI 0.66–0.70) than traditional clinical features (Team 1: AUC 0.69, 95% CI 0.65–0.73; Team 2: AUC 0.65, 95% CI 0.62–0.68). Our results suggest that gestures provide a granular method to objectively indicate surgical performance and outcomes. Application of this methodology to other surgeries may lead to discoveries on methods to improve surgery.