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Updated: Aug 8, 2026
Laparoscopic Common Bile Duct Exploration in Patients with a Previous History of Biliary Tract Surgery
Published on: February 10, 2023
Machine Learning-Based Analysis in the Management of Iatrogenic Bile Duct Injury During Cholecystectomy: a Nationwide
Victor Lopez-Lopez1, Javier Maupoey2, Rafael López-Andujar2
1Department of Surgery and Transplantation, Virgen de La Arrixaca Clinic and University Hospital, Murcian Institute of Biosanitary Research (IMIB), Ctra. Madrid-Cartagena, 30120, s/nEl Palmar, Murcia, Spain. victorrelopez@gmail.com.
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.
