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 ri...
| Autores: | ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; |
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| Tipo de documento: | Artículo |
| Estado: | Published version |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad de Barcelona |
| Repositorio: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/187831 |
| Acceso en línea: | https://hdl.handle.net/2445/187831 |
| Nivel de acceso: | Acceso abierto |
| Palabra clave: | Malalties dels conductes biliars Patologia quirúrgica Bile ducts diseases Surgical pathology |
| _version_ | 1878742808673648640 |
|---|---|
| author | Lopez Lopez, Victor |
| author2 | Maupoey, Javier López Andújar, Rafael Ramos Rubio, Emilio Mils, Kristel Martinez, Pedro Antonio Valdivieso, Andres Garcés Albir, Marina Sabater, Luis Díez Valladares, Luis Annese Pérez, Sergio Flores, Benito Brusadin, Roberto López Conesa, Asunción Cayuela, Valentin Martinez Cortijo, Sagrario Paterna, Sandra Serrablo Requejo, Alejandro Sánchez Cabús, Santiago González Gil, Antonio González Masía, Jose Antonio Loinaz, Carmelo Lucena, Jose Luis Pastor, Patricia Garcia Zamora, Cristina Calero, Alicia Valiente, Juan Minguillon, Antonio Rotellar Sastre, Fernando Ramia, Jose Manuel Alcazar, Cándido Aguilo, Javier Cutillas, Jose Kuemmerli, Christoph Ruiperez Valiente, Jose A. Robles Campos, Ricardo |
| 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 |
| author_browse | Aguilo, Javier Alcazar, Cándido Annese Pérez, Sergio Brusadin, Roberto Calero, Alicia Cayuela, Valentin Cutillas, Jose Díez Valladares, Luis Flores, Benito Garcia Zamora, Cristina Garcés Albir, Marina González Gil, Antonio González Masía, Jose Antonio Kuemmerli, Christoph Loinaz, Carmelo Lopez Lopez, Victor Lucena, Jose Luis López Andújar, Rafael López Conesa, Asunción Martinez Cortijo, Sagrario Martinez, Pedro Antonio Maupoey, Javier Mils, Kristel Minguillon, Antonio Pastor, Patricia Paterna, Sandra Ramia, Jose Manuel Ramos Rubio, Emilio Robles Campos, Ricardo Rotellar Sastre, Fernando Ruiperez Valiente, Jose A. Sabater, Luis Serrablo Requejo, Alejandro Sánchez Cabús, Santiago Valdivieso, Andres Valiente, Juan |
| author_facet | Lopez Lopez, Victor Maupoey, Javier López Andújar, Rafael Ramos Rubio, Emilio Mils, Kristel Martinez, Pedro Antonio Valdivieso, Andres Garcés Albir, Marina Sabater, Luis Díez Valladares, Luis Annese Pérez, Sergio Flores, Benito Brusadin, Roberto López Conesa, Asunción Cayuela, Valentin Martinez Cortijo, Sagrario Paterna, Sandra Serrablo Requejo, Alejandro Sánchez Cabús, Santiago González Gil, Antonio González Masía, Jose Antonio Loinaz, Carmelo Lucena, Jose Luis Pastor, Patricia Garcia Zamora, Cristina Calero, Alicia Valiente, Juan Minguillon, Antonio Rotellar Sastre, Fernando Ramia, Jose Manuel Alcazar, Cándido Aguilo, Javier Cutillas, Jose Kuemmerli, Christoph Ruiperez Valiente, Jose A. Robles Campos, Ricardo |
| author_role | author |
| collection | Dipòsit Digital de la UB |
| 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. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | ES_fca605fee84a6608e730d7638bfcb7b7 |
| institution | Universidad de Barcelona |
| instname_str | Universidad de Barcelona |
| network_acronym_str | ES |
| network_name_str | España |
| oai_identifier_str | oai:diposit.ub.edu:2445/187831 |
| publishDate | 2022 |
| publishDateSort | 2022 |
| publisher | Springer Science and Business Media LLC |
| publisherStr | Springer Science and Business Media LLC |
| reponame_str | Dipòsit Digital de la UB |
| spelling | Machine Learning-Based Analysis in the Management of Iatrogenic Bile Duct Injury During Cholecystectomy: a Nationwide Multicenter Study Lopez Lopez, Victor Maupoey, Javier López Andújar, Rafael Ramos Rubio, Emilio Mils, Kristel Martinez, Pedro Antonio Valdivieso, Andres Garcés Albir, Marina Sabater, Luis Díez Valladares, Luis Annese Pérez, Sergio Flores, Benito Brusadin, Roberto López Conesa, Asunción Cayuela, Valentin Martinez Cortijo, Sagrario Paterna, Sandra Serrablo Requejo, Alejandro Sánchez Cabús, Santiago González Gil, Antonio González Masía, Jose Antonio Loinaz, Carmelo Lucena, Jose Luis Pastor, Patricia Garcia Zamora, Cristina Calero, Alicia Valiente, Juan Minguillon, Antonio Rotellar Sastre, Fernando Ramia, Jose Manuel Alcazar, Cándido Aguilo, Javier Cutillas, Jose Kuemmerli, Christoph Ruiperez Valiente, Jose A. Robles Campos, Ricardo Malalties dels conductes biliars Patologia quirúrgica Bile ducts diseases Surgical pathology 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. Springer Science and Business Media LLC https://hdl.handle.net/2445/187831 |
| spellingShingle | Machine Learning-Based Analysis in the Management of Iatrogenic Bile Duct Injury During Cholecystectomy: a Nationwide Multicenter Study Lopez Lopez, Victor Malalties dels conductes biliars Patologia quirúrgica Bile ducts diseases Surgical pathology |
| status_str | publishedVersion |
| title | 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_short | 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 |
| topic | Malalties dels conductes biliars Patologia quirúrgica Bile ducts diseases Surgical pathology |
| topic_facet | Malalties dels conductes biliars Patologia quirúrgica Bile ducts diseases Surgical pathology |
| url | https://hdl.handle.net/2445/187831 |