Machine learning to predict major bleeding during anticoagulation for venous thromboembolism: possibilities and limitations

Detalhes bibliográficos
Autor(a) principal: Mora, Damián
Data de Publicação: 2023
Outros Autores: 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.
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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spelling 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.
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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