Machine learning to predict major bleeding during anticoagulation for venous thromboembolism: possibilities and limitations
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1111/bjh.18737 http://hdl.handle.net/11449/248586 |
Resumo: | Predictive tools for major bleeding (MB) using machine learning (ML) might be advantageous over traditional methods. We used data from the Registro Informatizado de Enfermedad TromboEmbólica (RIETE) to develop ML algorithms to identify patients with venous thromboembolism (VTE) at increased risk of MB during the first 3 months of anticoagulation. A total of 55 baseline variables were used as predictors. New data prospectively collected from the RIETE were used for further validation. The RIETE and VTE-BLEED scores were used for comparisons. External validation was performed with the COMMAND-VTE database. Learning was carried out with data from 49 587 patients, of whom 873 (1.8%) had MB. The best performing ML method was XGBoost. In the prospective validation cohort the sensitivity, specificity, positive predictive value and F1 score were: 33.2%, 93%, 10%, and 15.4% respectively. F1 value for the RIETE and VTE-BLEED scores were 8.6% and 6.4% respectively. In the external validation cohort the metrics were 10.3%, 87.6%, 3.5% and 5.2% respectively. In that cohort, the F1 value for the RIETE score was 17.3% and for the VTE-BLEED score 9.75%. The performance of the XGBoost algorithm was better than that from the RIETE and VTE-BLEED scores only in the prospective validation cohort, but not in the external validation cohort. |
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Machine learning to predict major bleeding during anticoagulation for venous thromboembolism: possibilities and limitationshaemorrhagemachine learningoutcomespulmonary embolismvenous thrombosisPredictive tools for major bleeding (MB) using machine learning (ML) might be advantageous over traditional methods. We used data from the Registro Informatizado de Enfermedad TromboEmbólica (RIETE) to develop ML algorithms to identify patients with venous thromboembolism (VTE) at increased risk of MB during the first 3 months of anticoagulation. A total of 55 baseline variables were used as predictors. New data prospectively collected from the RIETE were used for further validation. The RIETE and VTE-BLEED scores were used for comparisons. External validation was performed with the COMMAND-VTE database. Learning was carried out with data from 49 587 patients, of whom 873 (1.8%) had MB. The best performing ML method was XGBoost. In the prospective validation cohort the sensitivity, specificity, positive predictive value and F1 score were: 33.2%, 93%, 10%, and 15.4% respectively. F1 value for the RIETE and VTE-BLEED scores were 8.6% and 6.4% respectively. In the external validation cohort the metrics were 10.3%, 87.6%, 3.5% and 5.2% respectively. In that cohort, the F1 value for the RIETE score was 17.3% and for the VTE-BLEED score 9.75%. The performance of the XGBoost algorithm was better than that from the RIETE and VTE-BLEED scores only in the prospective validation cohort, but not in the external validation cohort.Department of Internal Medicine Hospital Virgen de la LuzInstitute of Technology Universidad de Castilla-La ManchaCardiovascular Medicine Division Brigham and Women's Hospital Harvard Medical SchoolThrombosis Research Group Brigham and Women's Hospital Harvard Medical SchoolYNHH/Yale Center for Outcomes Research and Evaluation (CORE)Cardiovascular Research Foundation (CRF)Department of Cardiovascular Medicine Graduate School of Medicine Kyoto UniversityDepartment of Angiology University Hospital ZurichCenter for Thrombosis and Hemostasis University Hospital MainzRespiratory Department Hospital Ramón y Cajal and Universidad de Alcalá (IRYCIS)CIBER de Enfermedades Respiratorias (CIBERES)Department of Internal Medicine Hospital General Universitario Gregorio MarañónDepartment of Internal Medicine Hospital Universitario Virgen de ArrixacaDepartment of Internal Medicine – Pulmonary Division Botucatu Medical School – São Paulo State University (UNESP)Department of Peripheral Vascular Diseases Rajaie Cardiovascular Medical and Research CenterChair of Thromboembolic Diseases Universidad Católica San Antonio de MurciaDepartment of Internal Medicine – Pulmonary Division Botucatu Medical School – São Paulo State University (UNESP)Hospital Virgen de la LuzUniversidad de Castilla-La ManchaHarvard Medical SchoolYNHH/Yale Center for Outcomes Research and Evaluation (CORE)Cardiovascular Research Foundation (CRF)Kyoto UniversityUniversity Hospital ZurichUniversity Hospital MainzHospital Ramón y Cajal and Universidad de Alcalá (IRYCIS)CIBER de Enfermedades Respiratorias (CIBERES)Hospital General Universitario Gregorio MarañónHospital Universitario Virgen de ArrixacaUniversidade Estadual Paulista (UNESP)Rajaie Cardiovascular Medical and Research CenterUniversidad Católica San Antonio de MurciaMora, DamiánMateo, JorgeNieto, José A.Bikdeli, BehnoodYamashita, YugoBarco, StefanoJimenez, DavidDemelo-Rodriguez, PabloRosa, VladimirYoo, Hugo Hyung Bok [UNESP]Sadeghipour, ParhamMonreal, ManuelAdarraga, M. D.Alberich-Conesa, A.Alonso-Carrillo, J.Agudo, P.Amado, C.Amorós, S.Arcelus, J. I.Ballaz, A.Barba, R.Barbagelata, C.Barrón, M.Barrón-Andrés, B.Blanco-Molina, A.Botella, E.Carrero-Arribas, R.Casado, I.Chasco, L.Criado, J.del Toro, J.De Ancos, C.De Juana-Izquierdo, C.Demelo-Rodríguez, P.Díaz-Brasero, A. M.Díaz-Pedroche, M. C.Díaz-Peromingo, J. A.Díaz-Simón, R.Dubois-Silva, A.Escribano, J. C.Espósito, F.Falgá, C.Farfán-Sedano, A. I.Fernández-Aracil, C.Fernández-Capitán, C.Fernández-Jiménez, B.Fernández-Muixi, J.Fernández-Reyes, J. L.Font, C.Francisco, I.Galeano-Valle, F.García, M. A.García de Herreros, M.García-Bragado, F.García-González, C.García-Ortega, A.Gavín-Sebastián, O.Gil-De Gómez, M.Gil-Díaz, A.Gómez-Cuervo, C.Gómez-Mosquera, A. M.González-Martínez, J.Grau, E.Guirado, L.Gutiérrez, J.Hernández-Blasco, L.Jaras, M. J.Jiménez, D.Jou, I.Joya, M. D.Lacruz, B.Lainez-Justo, S.Lecumberri, R.Lobo, J. L.López-De la Fuente, M.López-Jiménez, L.López-Miguel, P.López-Núñez, J. J.López-Reyes, R.López-Ruiz, A.López-Sáez, J. B.Lorente, M. A.Lorenzo, A.Lumbierres, M.Madridano, O.Maestre, A.Marcos, M.Martín-Guerra, J. M.Martín-Martos, F.Mas-Maresma, L.Mellado, M.Mena, E.Mercado, M. I.Moisés, J.Monreal, M.Muñoz-Blanco, A.Muñoz-Gamito, G.Nieto, J. A.Núñez-Fernández, M. J.Osorio, J.Otalora, S.Pacheco-Gómez, N.Parra, P.Pedrajas, J. M.Pérez-Ductor, C.Pérez-Pérez, J. L.Peris, M. L.Pesce, M. L.Porras, J. A.Poyo-Molina, J.Puchades, R.Riera-Mestre, A.Rivera-Civico, F.Rivera-Gallego, A.Roca, M.Rodríguez-Cobo, A.Rubio, C. M.Ruiz-Giménez, N.Ruiz-Ruiz, J.Salgueiro, G.Sancho, T.Sendín, V.Sigüenza, P.Soler, S.Suriñach, J. M.Tiberio, G.Tolosa, C.Torres, M. I.Trujillo-Santos, J.Uresandi, F.Usandizaga, E.Valle, R.Varona, J. F.Vela, J. R.Vidal, G.Villalobos, A.Villares, P.Ay, C.Nopp, S.Pabinger, I.Engelen, M.Verhamme, P.Verstraete, A.Yoo, H. H.B.Arguello, J. D.Montenegro, A. C.Roa, J.Malý, R.Accassat, S.Bertoletti, L.Bura-Riviere, A.Catella, J.Chopard, R.Couturaud, F.Espitia, O.Grange, C.Leclercq, B.Le Mao, R.Mahé, I.Moustafa, F.Plaisance, L.Poenou, G.Sarlon-Bartoli, G.Suchon, P.Versini, E.Schellong, S.Braester, A.Brenner, B.Kenet, G.Najib, D.Tzoran, I.Sadeghipour, P.Basaglia, M.Bilora, F.Bortoluzzi, C.Brandolin, B.Ciammaichella, M.Colaizzo, D.De Angelis, A.Dentali, F.Di Micco, P.Grandone, E.Imbalzano, E.Merla, S.Pesavento, R.Prandoni, P.Scarinzi, P.Siniscalchi, C.Taflaj, B.Tufano, A.Visonà, A.Vo Hong, N.Zalunardo, B.Kigitovica, D.Skride, A.Fonseca, S.Manuel, M.Meireles, J.Bosevski, M.Zdraveska, M.Bounameaux, H.Mazzolai, L.Aujayeb, A.Caprini, J. A.Weinberg, I.Bui, H. M.2023-07-29T13:48:05Z2023-07-29T13:48:05Z2023-06-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article971-981http://dx.doi.org/10.1111/bjh.18737British Journal of Haematology, v. 201, n. 5, p. 971-981, 2023.1365-21410007-1048http://hdl.handle.net/11449/24858610.1111/bjh.187372-s2.0-85151067665Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengBritish Journal of Haematologyinfo:eu-repo/semantics/openAccess2024-08-14T17:23:20Zoai:repositorio.unesp.br:11449/248586Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-14T17:23:20Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Machine learning to predict major bleeding during anticoagulation for venous thromboembolism: possibilities and limitations |
title |
Machine learning to predict major bleeding during anticoagulation for venous thromboembolism: possibilities and limitations |
spellingShingle |
Machine learning to predict major bleeding during anticoagulation for venous thromboembolism: possibilities and limitations Mora, Damián haemorrhage machine learning outcomes pulmonary embolism venous thrombosis |
title_short |
Machine learning to predict major bleeding during anticoagulation for venous thromboembolism: possibilities and limitations |
title_full |
Machine learning to predict major bleeding during anticoagulation for venous thromboembolism: possibilities and limitations |
title_fullStr |
Machine learning to predict major bleeding during anticoagulation for venous thromboembolism: possibilities and limitations |
title_full_unstemmed |
Machine learning to predict major bleeding during anticoagulation for venous thromboembolism: possibilities and limitations |
title_sort |
Machine learning to predict major bleeding during anticoagulation for venous thromboembolism: possibilities and limitations |
author |
Mora, Damián |
author_facet |
Mora, Damián Mateo, Jorge Nieto, José A. Bikdeli, Behnood Yamashita, Yugo Barco, Stefano Jimenez, David Demelo-Rodriguez, Pablo Rosa, Vladimir Yoo, Hugo Hyung Bok [UNESP] Sadeghipour, Parham Monreal, Manuel Adarraga, M. D. Alberich-Conesa, A. Alonso-Carrillo, J. Agudo, P. Amado, C. Amorós, S. Arcelus, J. I. Ballaz, A. Barba, R. Barbagelata, C. Barrón, M. Barrón-Andrés, B. Blanco-Molina, A. Botella, E. Carrero-Arribas, R. Casado, I. Chasco, L. Criado, J. del Toro, J. De Ancos, C. De Juana-Izquierdo, C. Demelo-Rodríguez, P. Díaz-Brasero, A. M. Díaz-Pedroche, M. C. Díaz-Peromingo, J. A. Díaz-Simón, R. Dubois-Silva, A. Escribano, J. C. Espósito, F. Falgá, C. Farfán-Sedano, A. I. Fernández-Aracil, C. Fernández-Capitán, C. Fernández-Jiménez, B. Fernández-Muixi, J. Fernández-Reyes, J. L. Font, C. Francisco, I. Galeano-Valle, F. García, M. A. García de Herreros, M. García-Bragado, F. García-González, C. García-Ortega, A. Gavín-Sebastián, O. Gil-De Gómez, M. Gil-Díaz, A. Gómez-Cuervo, C. Gómez-Mosquera, A. M. González-Martínez, J. Grau, E. Guirado, L. Gutiérrez, J. Hernández-Blasco, L. Jaras, M. J. Jiménez, D. Jou, I. Joya, M. D. Lacruz, B. Lainez-Justo, S. Lecumberri, R. Lobo, J. L. López-De la Fuente, M. López-Jiménez, L. López-Miguel, P. López-Núñez, J. J. López-Reyes, R. López-Ruiz, A. López-Sáez, J. B. Lorente, M. A. Lorenzo, A. Lumbierres, M. Madridano, O. Maestre, A. Marcos, M. Martín-Guerra, J. M. Martín-Martos, F. Mas-Maresma, L. Mellado, M. Mena, E. Mercado, M. I. Moisés, J. Monreal, M. Muñoz-Blanco, A. Muñoz-Gamito, G. Nieto, J. A. Núñez-Fernández, M. J. Osorio, J. Otalora, S. Pacheco-Gómez, N. Parra, P. Pedrajas, J. M. Pérez-Ductor, C. Pérez-Pérez, J. L. Peris, M. L. Pesce, M. L. Porras, J. A. Poyo-Molina, J. Puchades, R. Riera-Mestre, A. Rivera-Civico, F. Rivera-Gallego, A. Roca, M. Rodríguez-Cobo, A. Rubio, C. M. Ruiz-Giménez, N. Ruiz-Ruiz, J. Salgueiro, G. Sancho, T. Sendín, V. Sigüenza, P. Soler, S. Suriñach, J. M. Tiberio, G. Tolosa, C. Torres, M. I. Trujillo-Santos, J. Uresandi, F. Usandizaga, E. Valle, R. Varona, J. F. Vela, J. R. Vidal, G. Villalobos, A. Villares, P. Ay, C. Nopp, S. Pabinger, I. Engelen, M. Verhamme, P. Verstraete, A. Yoo, H. H.B. Arguello, J. D. Montenegro, A. C. Roa, J. Malý, R. Accassat, S. Bertoletti, L. Bura-Riviere, A. Catella, J. Chopard, R. Couturaud, F. Espitia, O. Grange, C. Leclercq, B. Le Mao, R. Mahé, I. Moustafa, F. Plaisance, L. Poenou, G. Sarlon-Bartoli, G. Suchon, P. Versini, E. Schellong, S. Braester, A. Brenner, B. Kenet, G. Najib, D. Tzoran, I. Sadeghipour, P. Basaglia, M. Bilora, F. Bortoluzzi, C. Brandolin, B. Ciammaichella, M. Colaizzo, D. De Angelis, A. Dentali, F. Di Micco, P. Grandone, E. Imbalzano, E. Merla, S. Pesavento, R. Prandoni, P. Scarinzi, P. Siniscalchi, C. Taflaj, B. Tufano, A. Visonà, A. Vo Hong, N. Zalunardo, B. Kigitovica, D. Skride, A. Fonseca, S. Manuel, M. Meireles, J. Bosevski, M. Zdraveska, M. Bounameaux, H. Mazzolai, L. Aujayeb, A. Caprini, J. A. Weinberg, I. Bui, H. M. |
author_role |
author |
author2 |
Mateo, Jorge Nieto, José A. Bikdeli, Behnood Yamashita, Yugo Barco, Stefano Jimenez, David Demelo-Rodriguez, Pablo Rosa, Vladimir Yoo, Hugo Hyung Bok [UNESP] Sadeghipour, Parham Monreal, Manuel Adarraga, M. D. Alberich-Conesa, A. Alonso-Carrillo, J. Agudo, P. Amado, C. Amorós, S. Arcelus, J. I. Ballaz, A. Barba, R. Barbagelata, C. Barrón, M. Barrón-Andrés, B. Blanco-Molina, A. Botella, E. Carrero-Arribas, R. Casado, I. Chasco, L. Criado, J. del Toro, J. De Ancos, C. De Juana-Izquierdo, C. Demelo-Rodríguez, P. Díaz-Brasero, A. M. Díaz-Pedroche, M. C. Díaz-Peromingo, J. A. Díaz-Simón, R. Dubois-Silva, A. Escribano, J. C. Espósito, F. Falgá, C. Farfán-Sedano, A. I. Fernández-Aracil, C. Fernández-Capitán, C. Fernández-Jiménez, B. Fernández-Muixi, J. Fernández-Reyes, J. L. Font, C. Francisco, I. Galeano-Valle, F. García, M. A. García de Herreros, M. García-Bragado, F. García-González, C. García-Ortega, A. Gavín-Sebastián, O. Gil-De Gómez, M. Gil-Díaz, A. Gómez-Cuervo, C. Gómez-Mosquera, A. M. González-Martínez, J. Grau, E. Guirado, L. Gutiérrez, J. Hernández-Blasco, L. Jaras, M. J. Jiménez, D. Jou, I. Joya, M. D. Lacruz, B. Lainez-Justo, S. Lecumberri, R. Lobo, J. L. López-De la Fuente, M. López-Jiménez, L. López-Miguel, P. López-Núñez, J. J. López-Reyes, R. López-Ruiz, A. López-Sáez, J. B. Lorente, M. A. Lorenzo, A. Lumbierres, M. Madridano, O. Maestre, A. Marcos, M. Martín-Guerra, J. M. Martín-Martos, F. Mas-Maresma, L. Mellado, M. Mena, E. Mercado, M. I. Moisés, J. Monreal, M. Muñoz-Blanco, A. Muñoz-Gamito, G. Nieto, J. A. Núñez-Fernández, M. J. Osorio, J. Otalora, S. Pacheco-Gómez, N. Parra, P. Pedrajas, J. M. Pérez-Ductor, C. Pérez-Pérez, J. L. Peris, M. L. Pesce, M. L. Porras, J. A. Poyo-Molina, J. Puchades, R. Riera-Mestre, A. Rivera-Civico, F. Rivera-Gallego, A. Roca, M. Rodríguez-Cobo, A. Rubio, C. M. Ruiz-Giménez, N. Ruiz-Ruiz, J. Salgueiro, G. Sancho, T. Sendín, V. Sigüenza, P. Soler, S. Suriñach, J. M. Tiberio, G. Tolosa, C. Torres, M. I. Trujillo-Santos, J. Uresandi, F. Usandizaga, E. Valle, R. Varona, J. F. Vela, J. R. Vidal, G. Villalobos, A. Villares, P. Ay, C. Nopp, S. Pabinger, I. Engelen, M. Verhamme, P. Verstraete, A. Yoo, H. H.B. Arguello, J. D. Montenegro, A. C. Roa, J. Malý, R. Accassat, S. Bertoletti, L. Bura-Riviere, A. Catella, J. Chopard, R. Couturaud, F. Espitia, O. Grange, C. Leclercq, B. Le Mao, R. Mahé, I. Moustafa, F. Plaisance, L. Poenou, G. Sarlon-Bartoli, G. Suchon, P. Versini, E. Schellong, S. Braester, A. Brenner, B. Kenet, G. Najib, D. Tzoran, I. Sadeghipour, P. Basaglia, M. Bilora, F. Bortoluzzi, C. Brandolin, B. Ciammaichella, M. Colaizzo, D. De Angelis, A. Dentali, F. Di Micco, P. Grandone, E. Imbalzano, E. Merla, S. Pesavento, R. Prandoni, P. Scarinzi, P. Siniscalchi, C. Taflaj, B. Tufano, A. Visonà, A. Vo Hong, N. Zalunardo, B. Kigitovica, D. Skride, A. Fonseca, S. Manuel, M. Meireles, J. Bosevski, M. Zdraveska, M. Bounameaux, H. Mazzolai, L. Aujayeb, A. Caprini, J. A. Weinberg, I. Bui, H. M. |
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 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 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 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 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 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.contributor.none.fl_str_mv |
Hospital Virgen de la Luz Universidad de Castilla-La Mancha Harvard Medical School YNHH/Yale Center for Outcomes Research and Evaluation (CORE) Cardiovascular Research Foundation (CRF) Kyoto University University Hospital Zurich University Hospital Mainz Hospital Ramón y Cajal and Universidad de Alcalá (IRYCIS) CIBER de Enfermedades Respiratorias (CIBERES) Hospital General Universitario Gregorio Marañón Hospital Universitario Virgen de Arrixaca Universidade Estadual Paulista (UNESP) Rajaie Cardiovascular Medical and Research Center Universidad Católica San Antonio de Murcia |
dc.contributor.author.fl_str_mv |
Mora, Damián Mateo, Jorge Nieto, José A. Bikdeli, Behnood Yamashita, Yugo Barco, Stefano Jimenez, David Demelo-Rodriguez, Pablo Rosa, Vladimir Yoo, Hugo Hyung Bok [UNESP] Sadeghipour, Parham Monreal, Manuel Adarraga, M. D. Alberich-Conesa, A. Alonso-Carrillo, J. Agudo, P. Amado, C. Amorós, S. Arcelus, J. I. Ballaz, A. Barba, R. Barbagelata, C. Barrón, M. Barrón-Andrés, B. Blanco-Molina, A. Botella, E. Carrero-Arribas, R. Casado, I. Chasco, L. Criado, J. del Toro, J. De Ancos, C. De Juana-Izquierdo, C. Demelo-Rodríguez, P. Díaz-Brasero, A. M. Díaz-Pedroche, M. C. Díaz-Peromingo, J. A. Díaz-Simón, R. Dubois-Silva, A. Escribano, J. C. Espósito, F. Falgá, C. Farfán-Sedano, A. I. Fernández-Aracil, C. Fernández-Capitán, C. Fernández-Jiménez, B. Fernández-Muixi, J. Fernández-Reyes, J. L. Font, C. Francisco, I. Galeano-Valle, F. García, M. A. García de Herreros, M. García-Bragado, F. García-González, C. García-Ortega, A. Gavín-Sebastián, O. Gil-De Gómez, M. Gil-Díaz, A. Gómez-Cuervo, C. Gómez-Mosquera, A. M. González-Martínez, J. Grau, E. Guirado, L. Gutiérrez, J. Hernández-Blasco, L. Jaras, M. J. Jiménez, D. Jou, I. Joya, M. D. Lacruz, B. Lainez-Justo, S. Lecumberri, R. Lobo, J. L. López-De la Fuente, M. López-Jiménez, L. López-Miguel, P. López-Núñez, J. J. López-Reyes, R. López-Ruiz, A. López-Sáez, J. B. Lorente, M. A. Lorenzo, A. Lumbierres, M. Madridano, O. Maestre, A. Marcos, M. Martín-Guerra, J. M. Martín-Martos, F. Mas-Maresma, L. Mellado, M. Mena, E. Mercado, M. I. Moisés, J. Monreal, M. Muñoz-Blanco, A. Muñoz-Gamito, G. Nieto, J. A. Núñez-Fernández, M. J. Osorio, J. Otalora, S. Pacheco-Gómez, N. Parra, P. Pedrajas, J. M. Pérez-Ductor, C. Pérez-Pérez, J. L. Peris, M. L. Pesce, M. L. Porras, J. A. Poyo-Molina, J. Puchades, R. Riera-Mestre, A. Rivera-Civico, F. Rivera-Gallego, A. Roca, M. Rodríguez-Cobo, A. Rubio, C. M. Ruiz-Giménez, N. Ruiz-Ruiz, J. Salgueiro, G. Sancho, T. Sendín, V. Sigüenza, P. Soler, S. Suriñach, J. M. Tiberio, G. Tolosa, C. Torres, M. I. Trujillo-Santos, J. Uresandi, F. Usandizaga, E. Valle, R. Varona, J. F. Vela, J. R. Vidal, G. Villalobos, A. Villares, P. Ay, C. Nopp, S. Pabinger, I. Engelen, M. Verhamme, P. Verstraete, A. Yoo, H. H.B. Arguello, J. D. Montenegro, A. C. Roa, J. Malý, R. Accassat, S. Bertoletti, L. Bura-Riviere, A. Catella, J. Chopard, R. Couturaud, F. Espitia, O. Grange, C. Leclercq, B. Le Mao, R. Mahé, I. Moustafa, F. Plaisance, L. Poenou, G. Sarlon-Bartoli, G. Suchon, P. Versini, E. Schellong, S. Braester, A. Brenner, B. Kenet, G. Najib, D. Tzoran, I. Sadeghipour, P. Basaglia, M. Bilora, F. Bortoluzzi, C. Brandolin, B. Ciammaichella, M. Colaizzo, D. De Angelis, A. Dentali, F. Di Micco, P. Grandone, E. Imbalzano, E. Merla, S. Pesavento, R. Prandoni, P. Scarinzi, P. Siniscalchi, C. Taflaj, B. Tufano, A. Visonà, A. Vo Hong, N. Zalunardo, B. Kigitovica, D. Skride, A. Fonseca, S. Manuel, M. Meireles, J. Bosevski, M. Zdraveska, M. Bounameaux, H. Mazzolai, L. Aujayeb, A. Caprini, J. A. Weinberg, I. Bui, H. M. |
dc.subject.por.fl_str_mv |
haemorrhage machine learning outcomes pulmonary embolism venous thrombosis |
topic |
haemorrhage machine learning outcomes pulmonary embolism venous thrombosis |
description |
Predictive tools for major bleeding (MB) using machine learning (ML) might be advantageous over traditional methods. We used data from the Registro Informatizado de Enfermedad TromboEmbólica (RIETE) to develop ML algorithms to identify patients with venous thromboembolism (VTE) at increased risk of MB during the first 3 months of anticoagulation. A total of 55 baseline variables were used as predictors. New data prospectively collected from the RIETE were used for further validation. The RIETE and VTE-BLEED scores were used for comparisons. External validation was performed with the COMMAND-VTE database. Learning was carried out with data from 49 587 patients, of whom 873 (1.8%) had MB. The best performing ML method was XGBoost. In the prospective validation cohort the sensitivity, specificity, positive predictive value and F1 score were: 33.2%, 93%, 10%, and 15.4% respectively. F1 value for the RIETE and VTE-BLEED scores were 8.6% and 6.4% respectively. In the external validation cohort the metrics were 10.3%, 87.6%, 3.5% and 5.2% respectively. In that cohort, the F1 value for the RIETE score was 17.3% and for the VTE-BLEED score 9.75%. The performance of the XGBoost algorithm was better than that from the RIETE and VTE-BLEED scores only in the prospective validation cohort, but not in the external validation cohort. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-29T13:48:05Z 2023-07-29T13:48:05Z 2023-06-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1111/bjh.18737 British Journal of Haematology, v. 201, n. 5, p. 971-981, 2023. 1365-2141 0007-1048 http://hdl.handle.net/11449/248586 10.1111/bjh.18737 2-s2.0-85151067665 |
url |
http://dx.doi.org/10.1111/bjh.18737 http://hdl.handle.net/11449/248586 |
identifier_str_mv |
British Journal of Haematology, v. 201, n. 5, p. 971-981, 2023. 1365-2141 0007-1048 10.1111/bjh.18737 2-s2.0-85151067665 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
British Journal of Haematology |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
971-981 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128156434956288 |