Machine Learning-Based Analysis in the Management of Iatrogenic Bile Duct Injury During Cholecystectomy: a Nationwide Multicenter Study
Víctor López‐López, Javier Maupoey, Rafael López‐Andújar, Emilio Ramos, Kristel Mils, Pedro Antonio Martinez, Andrés Valdivieso, Marina Garcés‐Albir, Luís Sabater, Luis Díez Valladares, Sergio Annese Pérez, Benito Flores, Roberto Brusadín, Asunción López Conesa, V. Cayuela, Sagrario Martínez Cortijo, Sandra Paterna, Alejando Serrablo, Santiago Sánchez-Cabús, Antonio González Gil, José Antonio González Masiá, Carmelo Loinaz, Jose Luis Lucena, Patricia Pastor, Cristina Garcia-Zamora, Alicia Calero, Juan Valiente, Antonio Minguillón, Fernando Rotellar, José Manuel Ramia, C. Alcázar, Javier Aguiló, Jose Cutillas, Christoph Kuemmerli, José A. Ruipérez‐Valiente, R Robles
Abstract
BACKGROUND: Iatrogenic bile duct injury (IBDI) is a challenging surgical complication. IBDI management can be guided by artificial intelligence models. Our study identified the factors associated with successful initial repair of IBDI and predicted the success of definitive repair based on patient risk levels. METHODS: This is a retrospective multi-institution cohort of patients with IBDI after cholecystectomy conducted between 1990 and 2020. We implemented a decision tree analysis to determine the factors that contribute to successful initial repair and developed a risk-scoring model based on the Comprehensive Complication Index. RESULTS: We analyzed 748 patients across 22 hospitals. Our decision tree model was 82.8% accurate in predicting the success of the initial repair. Non-type E (p < 0.01), treatment in specialized centers (p < 0.01), and surgical repair (p < 0.001) were associated with better prognosis. The risk-scoring model was 82.3% (79.0-85.3%, 95% confidence interval [CI]) and 71.7% (63.8-78.7%, 95% CI) accurate in predicting success in the development and validation cohorts, respectively. Surgical repair, successful initial repair, and repair between 2 and 6 weeks were associated with better outcomes. DISCUSSION: Machine learning algorithms for IBDI are a novel tool may help to improve the decision-making process and guide management of these patients.