학술논문
Machine Learning-Based Analysis in the Management of Iatrogenic Bile Duct Injury During Cholecystectomy: a Nationwide Multicenter Study
Document Type
Original Paper
Author
Lopez-Lopez, Victor; Maupoey, Javier; López-Andujar, Rafael; Ramos, Emilio; Mils, Kristel; Martinez, Pedro Antonio; Valdivieso, Andres; Garcés-Albir, Marina; Sabater, Luis; Valladares, Luis Díez; Pérez, Sergio Annese; Flores, Benito; Brusadin, Roberto; Conesa, Asunción López; Cayuela, Valentin; Cortijo, Sagrario Martinez; Paterna, Sandra; Serrablo, Alejando; Sánchez-Cabús, Santiago; Gil, Antonio González; Masía, Jose Antonio González; Loinaz, Carmelo; Lucena, Jose Luis; Pastor, Patricia; Garcia-Zamora, Cristina; Calero, Alicia; Valiente, Juan; Minguillon, Antonio; Rotellar, Fernando; Ramia, Jose Manuel; Alcazar, Cándido; Aguilo, Javier; Cutillas, Jose; Kuemmerli, Christoph; Ruiperez-Valiente, Jose A.; Robles-Campos, Ricardo
Source
Journal of Gastrointestinal Surgery. 26(8):1713-1723
Subject
Language
English
ISSN
1091-255X
1873-4626
1873-4626
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.