2026-04-17T05:22:04Zhttps://recercat.cat/oai/request

oai:recercat.cat:2445/1878312026-03-07T01:17:01Zcom_2072_1057col_2072_478916col_2072_478917

00925njm 22002777a 4500 dc Lopez Lopez, Victor author Maupoey, Javier author López Andújar, Rafael author Ramos Rubio, Emilio author Mils, Kristel author Martinez, Pedro Antonio author Valdivieso, Andres author Garcés Albir, Marina author Sabater, Luis author Díez Valladares, Luis author Annese Pérez, Sergio author Flores, Benito author Brusadin, Roberto author López Conesa, Asunción author Cayuela, Valentin author Martinez Cortijo, Sagrario author Paterna, Sandra author Serrablo Requejo, Alejandro author Sánchez Cabús, Santiago author González Gil, Antonio author González Masía, Jose Antonio author Loinaz, Carmelo author Lucena, Jose Luis author Pastor, Patricia author Garcia Zamora, Cristina author Calero, Alicia author Valiente, Juan author Minguillon, Antonio author Rotellar Sastre, Fernando author Ramia, Jose Manuel author Alcazar, Cándido author Aguilo, Javier author Cutillas, Jose author Kuemmerli, Christoph author Ruiperez Valiente, Jose A. author Robles Campos, Ricardo author 2022-07-18T17:12:30Z 2022-07-18T17:12:30Z 2022-07-05 2022-07-18T09:35:37Z 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. Malalties dels conductes biliars Patologia quirúrgica Bile ducts diseases Surgical pathology Machine Learning-Based Analysis in the Management of Iatrogenic Bile Duct Injury During Cholecystectomy: a Nationwide Multicenter Study