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

Identificador:  imarina:9295150

Handle: https://hdl.handle.net/20.500.11797/imarina9295150

Autors:  Mora D; Mateo J; Nieto JA; Bikdeli B; Yamashita Y; Barco S; Jimenez D; Demelo-Rodriguez P; Rosa V; Yoo HHB; Sadeghipour P; Monreal M; Adarraga MD; Alberich-Conesa A; Alonso-Carrillo J; Agudo P; Amado C; Amorós S; Arcelus JI; 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 AM; Díaz-Pedroche MC; Díaz-Peromingo JA; Díaz-Simón R; Dubois-Silva A; Escribano JC; Espósito F; Falgá C; Farfán-Sedano AI; Fernández-Aracil C; Fernández-Capitán C; Fernández-Jiménez B; Fernández-Muixi J; Fernández-Reyes JL; Font C; Francisco I; Galeano-Valle F; García MA; 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 AM; González-Martínez J; Grau E; Guirado L; Gutiérrez J; Hernández-Blasco L; Jaras MJ; Jiménez D; Jou I; Joya MD; Lacruz B; Lainez-Justo S; Lecumberri R; Lobo JL; López-De la Fuente M; López-Jiménez L; López-Miguel P; López-Núñez JJ; López-Reyes R; López-Ruiz A; López-Sáez JB; Lorente MA; Lorenzo A; Lumbierres M; Madridano O; Maestre A; Marcos M; Martín-Guerra JM; Martín-Martos F; Mas-Maresma L; Mellado M; Mena E; Mercado MI; Moisés J; Monreal M; Muñoz-Blanco A; Muñoz-Gamito G; Nieto JA; Núñez-Fernández MJ; Osorio J

Resum:
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.