A machine learning analysis of difficulty scoring systems for laparoscopic liver surgery

Andrea Ruzzenente; Fabio Bagante; Edoardo Poletto; Tommaso Campagnaro; Simone Conci; Mario De Bellis; Corrado Pedrazzani; Alfredo Guglielmi

doi:10.1007/s00464-022-09322-7

A machine learning analysis of difficulty scoring systems for laparoscopic liver surgery

Surg Endosc. 2022 Dec;36(12):8869-8880. doi: 10.1007/s00464-022-09322-7. Epub 2022 May 23.

Authors

Andrea Ruzzenente¹, Fabio Bagante², Edoardo Poletto², Tommaso Campagnaro², Simone Conci², Mario De Bellis², Corrado Pedrazzani², Alfredo Guglielmi²

Affiliations

¹ Department of Surgery, Dentistry, Gynecology and Pediatrics, Division of General and Hepato-Biliary Surgery, University of Verona, P. le L.A. Scuro, 37134, Verona, Italy. andrea.ruzzenente@univr.it.
² Department of Surgery, Dentistry, Gynecology and Pediatrics, Division of General and Hepato-Biliary Surgery, University of Verona, P. le L.A. Scuro, 37134, Verona, Italy.

Abstract

Introduction: In the last decade, several difficulty scoring systems (DSS) have been proposed to predict technical difficulty in laparoscopic liver resections (LLR). The present study aimed to investigate the ability of four DSS for LLR to predict operative, short-term, and textbook outcomes.

Methods: Patients who underwent LLR at a single tertiary referral center from January 2014 to June 2020 were included in the present study. Four DSS for LLR (Halls, Hasegawa, Kawaguchi, and Iwate) were investigated to test their ability to predict operative and postoperative complications. Machine learning algorithms were used to identify the most important DSS associated with operative and short-term outcomes.

Results: A total of 346 patients were included in the analysis, 28 (8.1%) patients were converted to open surgery. A total of 13 patients (3.7%) had severe (Clavien-Dindo ≥ 3) complications; the incidence of prolonged length of stay (> 5 days) was 39.3% (n = 136). No patients died within 90 days after the surgery. According to Halls, Hasegawa, Kawaguchi, and Iwate scores, 65 (18.8%), 59 (17.1%), 57 (16.5%), and 112 (32.4%) patients underwent high difficulty LLR, respectively. In accordance with a random forest algorithm, the Kawaguchi DSS predicted prolonged length of stay, high blood loss, and conversions and was the best performing DSS in predicting postoperative outcomes. Iwate DSS was the most important variable associated with operative time, while Halls score was the most important DSS predicting textbook outcomes. No one of the DSS investigated was associated with the occurrence of complication.

Conclusions: According to our results DDS are significantly related to surgical complexity and short-term outcomes, Kawaguchi and Iwate DSS showed the best performance in predicting operative outcomes, while Halls score was the most important variable in predicting textbook outcome. Interestingly, none of the DSS showed any correlation with or importance in predicting overall and severe postoperative complications.

Keywords: Difficulty scoring system; Laparoscopic liver resection; Machine learning; Patient selection; Textbook outcome.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Hepatectomy / methods
Humans
Laparoscopy* / methods
Length of Stay
Liver Neoplasms* / surgery
Machine Learning
Postoperative Complications / epidemiology
Postoperative Complications / etiology
Postoperative Complications / surgery
Retrospective Studies