Development and Validation of a Machine Learning Prediction Model for Textbook Outcome in Liver Surgery: Results From a Multicenter, International Cohort

Development and Validation of a Machine Learning Prediction Model for Textbook Outcome in Liver Surgery: Results From a Multicenter, International Cohort

Objective:. This study aimed to (1) develop a machine learning (ML) model that predicts the textbook outcome in liver surgery (TOLS) using preoperative variables and (2) validate the TOLS criteria by determining whether TOLS is associated with long-term survival after hepatectomy. Background:. Textb...

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Main Authors: Jane Wang, MD, Amir Ashraf Ganjouei, MD, MPH, Taizo Hibi, MD, PhD, Nuria Lluis, MD, PhD, Camilla Gomes, MD, Fernanda Romero-Hernandez, MD, Han Yin, BA, Lucia Calthorpe, MD, Yukiyasu Okamura, MD, PhD, Yuta Abe, MD, PhD, Shogo Tanaka, MD, PhD, Minoru Tanabe, MD, PhD, Zeniche Morise, MD, PhD, Horacio Asbun, MD, PhD, David Geller, MD, Mohammed Abu Hilal, MD, PhD, Mohamed Adam, MD, Adnan Alseidi, MD, EdM, International Hepatectomy Study Group, Alison Baskin, Annie Wong-On-Wing, Annie Yang, Devesh Sharma, Taisuke Imamura, Masanori Nakamura, Yuya Miura, Koki Hayashi, Masatsugu Ishii, Keita Shimata, Kazuya Hirukawa, Hiroki Ueda, June S. Peng, Lucas Thornblade, Kenzo Hirose, Kimberly Kirkwood, Eric Nakakura, Carlos Corvera, Ajay Maker
Format: Article
Language:English
Published: Wolters Kluwer Health 2025-03-01
Series:Annals of Surgery Open
Online Access:http://journals.lww.com/10.1097/AS9.0000000000000539
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Summary:Objective:. This study aimed to (1) develop a machine learning (ML) model that predicts the textbook outcome in liver surgery (TOLS) using preoperative variables and (2) validate the TOLS criteria by determining whether TOLS is associated with long-term survival after hepatectomy. Background:. Textbook outcome is a composite measure that combines several favorable outcomes into a single metric and represents the optimal postoperative course. Recently, an expert panel of surgeons proposed a Delphi consensus-based definition of TOLS. Methods:. Adult patients who underwent hepatectomies were identified from a multicenter, international cohort (2010–2022). After data preprocessing and train-test splitting (80:20), 4 models for predicting TOLS were trained and tested. Following model optimization, the performance of the models was evaluated using receiver operating characteristic curves, and a web-based calculator was developed. In addition, a multivariable Cox proportional hazards analysis was conducted to determine the association between TOLS and overall survival (OS). Results:. A total of 2059 patients were included, with 62.8% meeting the criteria for TOLS. The XGBoost model, which had the best performance with an area under the curve of 0.73, was chosen for the web-based calculator. The most predictive variables for having TOLS were a minimally invasive approach, fewer lesions, lower Charlson Comorbidity Index, lower preoperative creatinine levels, and smaller lesions. In the multivariable analysis, having TOLS was associated with improved OS (hazard ratio = 0.82, P = 0.015). Conclusions:. Our ML model can predict TOLS with acceptable discrimination. We validated the TOLS criteria by demonstrating a significant association with improved OS, thus supporting their use in informing patient care.
ISSN:2691-3593

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