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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Autores: Lopez-Lopez V, Maupoey J, López-Andujar R, Ramos E, Mils K, Martinez PA, Valdivieso A, Garcés-Albir M, Sabater L, Valladares LD, Pérez SA, Flores B, Brusadin R, Conesa AL, Cayuela V, Cortijo SM, Paterna S, Serrablo A, Sánchez-Cabús S, Gil AG, Masía 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
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Instituto de Investigación Biomédica y Sanitaria de Alicante (ISABIAL)
Repositorio:r-ISABIAL. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica y Sanitaria de Alicante
OAI Identifier:oai:isabial.fundanetsuite.com:p9078
Acceso en línea:https://isabial.portalinvestigacion.com/publicaciones9078
Access Level:acceso abierto
Palabra clave:Artificial neural network
Cholecystectomy
Iatrogenic bile duct injury
Machine learning
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spelling Machine Learning-Based Analysis in the Management of Iatrogenic Bile Duct Injury During Cholecystectomy: a Nationwide Multicenter Study.Lopez-Lopez VMaupoey JLópez-Andujar RRamos EMils KMartinez PAValdivieso AGarcés-Albir MSabater LValladares LDPérez SAFlores BBrusadin RConesa ALCayuela VCortijo SMPaterna SSerrablo ASánchez-Cabús SGil AGMasía JAGLoinaz CLucena JLPastor PGarcia-Zamora CCalero AValiente JMinguillon ARotellar FRamia JMAlcazar CAguilo JCutillas JKuemmerli CRuiperez-Valiente JARobles-Campos RArtificial neural networkCholecystectomyIatrogenic bile duct injuryMachine learningBACKGROUND: 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.ELSEVIER SCIENCE INC2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://isabial.portalinvestigacion.com/publicaciones9078JOURNAL OF GASTROINTESTINAL SURGERYISSN: 1091255XISSNe: 18734626reponame:r-ISABIAL. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica y Sanitaria de Alicanteinstname:Instituto de Investigación Biomédica y Sanitaria de Alicante (ISABIAL)Inglésinfo:eu-repo/semantics/openAccessoai:isabial.fundanetsuite.com:p90782026-06-12T10:20:37Z
dc.title.none.fl_str_mv Machine Learning-Based Analysis in the Management of Iatrogenic Bile Duct Injury During Cholecystectomy: a Nationwide Multicenter Study.
title Machine Learning-Based Analysis in the Management of Iatrogenic Bile Duct Injury During Cholecystectomy: a Nationwide Multicenter Study.
spellingShingle Machine Learning-Based Analysis in the Management of Iatrogenic Bile Duct Injury During Cholecystectomy: a Nationwide Multicenter Study.
Lopez-Lopez V
Artificial neural network
Cholecystectomy
Iatrogenic bile duct injury
Machine learning
title_short Machine Learning-Based Analysis in the Management of Iatrogenic Bile Duct Injury During Cholecystectomy: a Nationwide Multicenter Study.
title_full Machine Learning-Based Analysis in the Management of Iatrogenic Bile Duct Injury During Cholecystectomy: a Nationwide Multicenter Study.
title_fullStr Machine Learning-Based Analysis in the Management of Iatrogenic Bile Duct Injury During Cholecystectomy: a Nationwide Multicenter Study.
title_full_unstemmed Machine Learning-Based Analysis in the Management of Iatrogenic Bile Duct Injury During Cholecystectomy: a Nationwide Multicenter Study.
title_sort Machine Learning-Based Analysis in the Management of Iatrogenic Bile Duct Injury During Cholecystectomy: a Nationwide Multicenter Study.
dc.creator.none.fl_str_mv Lopez-Lopez V
Maupoey J
López-Andujar R
Ramos E
Mils K
Martinez PA
Valdivieso A
Garcés-Albir M
Sabater L
Valladares LD
Pérez SA
Flores B
Brusadin R
Conesa AL
Cayuela V
Cortijo SM
Paterna S
Serrablo A
Sánchez-Cabús S
Gil AG
Masía 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
author Lopez-Lopez V
author_facet Lopez-Lopez V
Maupoey J
López-Andujar R
Ramos E
Mils K
Martinez PA
Valdivieso A
Garcés-Albir M
Sabater L
Valladares LD
Pérez SA
Flores B
Brusadin R
Conesa AL
Cayuela V
Cortijo SM
Paterna S
Serrablo A
Sánchez-Cabús S
Gil AG
Masía 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
author_role author
author2 Maupoey J
López-Andujar R
Ramos E
Mils K
Martinez PA
Valdivieso A
Garcés-Albir M
Sabater L
Valladares LD
Pérez SA
Flores B
Brusadin R
Conesa AL
Cayuela V
Cortijo SM
Paterna S
Serrablo A
Sánchez-Cabús S
Gil AG
Masía 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
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Artificial neural network
Cholecystectomy
Iatrogenic bile duct injury
Machine learning
topic Artificial neural network
Cholecystectomy
Iatrogenic bile duct injury
Machine learning
description 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.
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://isabial.portalinvestigacion.com/publicaciones9078
url https://isabial.portalinvestigacion.com/publicaciones9078
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv ELSEVIER SCIENCE INC
publisher.none.fl_str_mv ELSEVIER SCIENCE INC
dc.source.none.fl_str_mv JOURNAL OF GASTROINTESTINAL SURGERY
ISSN: 1091255X
ISSNe: 18734626
reponame:r-ISABIAL. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica y Sanitaria de Alicante
instname:Instituto de Investigación Biomédica y Sanitaria de Alicante (ISABIAL)
instname_str Instituto de Investigación Biomédica y Sanitaria de Alicante (ISABIAL)
reponame_str r-ISABIAL. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica y Sanitaria de Alicante
collection r-ISABIAL. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica y Sanitaria de Alicante
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869422199785390081
score 15,811543

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