Machine Learning-Based Analysis in the Management of Iatrogenic Bile Duct Injury During Cholecystectomy: a Nationwide Multicenter Study
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Published:2022-07-05
Issue:8
Volume:26
Page:1713-1723
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ISSN:1091-255X
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Container-title:Journal of Gastrointestinal Surgery
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language:en
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Short-container-title:J Gastrointest Surg
Author:
Lopez-Lopez VictorORCID, 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
Abstract
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.
Funder
Universidad de Murcia
Publisher
Springer Science and Business Media LLC
Subject
Gastroenterology,Surgery
Reference30 articles.
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