Machine Learning-Based Analysis in the Management of Iatrogenic Bile Duct Injury During Cholecystectomy: a Nationwide Multicenter Study.

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 r...

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Bibliographic Details
Authors: Lopez-Lopez, V, Maupoey, J, Lopez-Andujar, R, Ramos, E, Mils, K, Martinez, PA, Valdivieso, A, Garces-Albir, M, Sabater, L, Valladares, LD, Perez, SA, Flores, B, Brusadin, R, Conesa, AL, Cayuela, V, Cortijo, SM, Paterna, S, Serrablo, A, Sanchez-Cabus, S, Gil, AG, Masia, JAG, Loinaz, C, Lucena, JL, Pastor, P, Garcia-Zamora, C, Calero, A, Valiente, J, Minguillon, A, Rotellar, F, Ramia, JM, Alcazar, C, Aguilo, J, Cutillas, J, Kuemmerli, C, Ruiperez-Valiente, JA, Robles-Campos, R
Format: article
Status:Published version
Publication Date:2022
Country:España
Institution:INCLIVA
Repository:r-INCLIVA. Repositorio Institucional de Producción Científica de INCLIVA
OAI Identifier:oai:incliva.fundanetsuite.com:p16815
Online Access:https://incliva.portalinvestigacion.com/publicaciones/16815
Access Level:Open access
Keyword:Artificial neural network
Cholecystectomy
Iatrogenic bile duct injury
Machine learning
Description
Summary: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.

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