Fabrizio D'Ascenzo1, Ovidio De Filippo2, Guglielmo Gallone2, Gianluca Mittone3, Marco Agostino Deriu4, Mario Iannaccone5, Albert Ariza-Solé6, Christoph Liebetrau7, Sergio Manzano-Fernández8, Giorgio Quadri9, Tim Kinnaird10, Gianluca Campo11, Jose Paulo Simao Henriques12, James M Hughes13, Alberto Dominguez-Rodriguez14, Marco Aldinucci3, Umberto Morbiducci4, Giuseppe Patti15, Sergio Raposeiras-Roubin16, Emad Abu-Assi16, Gaetano Maria De Ferrari2. 1. Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza, Turin, Italy; Cardiology, Department of Medical Sciences, University of Turin, Turin, Italy. Electronic address: fabrizio.dascenzo@gmail.com. 2. Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza, Turin, Italy; Cardiology, Department of Medical Sciences, University of Turin, Turin, Italy. 3. Department of Computer Science, University of Turin, Turin, Italy. 4. Polito BIO Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy. 5. Department of Cardiology, S G Bosco Hospital, Turin, Italy. 6. Department of Cardiology, University Hospital de Bellvitge, Barcelona, Spain. 7. Kerckhoff Heart and Thorax Center, Frankfurt, Germany. 8. Department of Cardiology, University Hospital Virgen Arrtixaca, Murcia, Spain. 9. Interventional Cardiology Unit, Degli Infermi Hospital, Turin, Italy. 10. Cardiology Department, University Hospital of Wales, Cardiff, UK. 11. Azienda Ospedaliera Universitaria di Ferrara, Ferrara, Italy. 12. University of Amsterdam, Academic Medical Center, Amsterdam, Netherlands. 13. Candiolo Cancer Institute, FPO - IRCCS, Turin, Italy. 14. Servicio de Cardiologìa, Hospital Universitario de Canarias, Santa Cruz de Tenerife, Spain. 15. Catheterization Laboratory, Maggiore della Carità Hospital, Novara, Italy. 16. Department of Cardiology, University Hospital Álvaro Cunqueiro, Vigo, Spain.
Abstract
BACKGROUND: The accuracy of current prediction tools for ischaemic and bleeding events after an acute coronary syndrome (ACS) remains insufficient for individualised patient management strategies. We developed a machine learning-based risk stratification model to predict all-cause death, recurrent acute myocardial infarction, and major bleeding after ACS. METHODS: Different machine learning models for the prediction of 1-year post-discharge all-cause death, myocardial infarction, and major bleeding (defined as Bleeding Academic Research Consortium type 3 or 5) were trained on a cohort of 19 826 adult patients with ACS (split into a training cohort [80%] and internal validation cohort [20%]) from the BleeMACS and RENAMI registries, which included patients across several continents. 25 clinical features routinely assessed at discharge were used to inform the models. The best-performing model for each study outcome (the PRAISE score) was tested in an external validation cohort of 3444 patients with ACS pooled from a randomised controlled trial and three prospective registries. Model performance was assessed according to a range of learning metrics including area under the receiver operating characteristic curve (AUC). FINDINGS: The PRAISE score showed an AUC of 0·82 (95% CI 0·78-0·85) in the internal validation cohort and 0·92 (0·90-0·93) in the external validation cohort for 1-year all-cause death; an AUC of 0·74 (0·70-0·78) in the internal validation cohort and 0·81 (0·76-0·85) in the external validation cohort for 1-year myocardial infarction; and an AUC of 0·70 (0·66-0·75) in the internal validation cohort and 0·86 (0·82-0·89) in the external validation cohort for 1-year major bleeding. INTERPRETATION: A machine learning-based approach for the identification of predictors of events after an ACS is feasible and effective. The PRAISE score showed accurate discriminative capabilities for the prediction of all-cause death, myocardial infarction, and major bleeding, and might be useful to guide clinical decision making. FUNDING: None.
BACKGROUND: The accuracy of current prediction tools for ischaemic and bleeding events after an acute coronary syndrome (ACS) remains insufficient for individualised patient management strategies. We developed a machine learning-based risk stratification model to predict all-cause death, recurrent acute myocardial infarction, and major bleeding after ACS. METHODS: Different machine learning models for the prediction of 1-year post-discharge all-cause death, myocardial infarction, and major bleeding (defined as Bleeding Academic Research Consortium type 3 or 5) were trained on a cohort of 19 826 adult patients with ACS (split into a training cohort [80%] and internal validation cohort [20%]) from the BleeMACS and RENAMI registries, which included patients across several continents. 25 clinical features routinely assessed at discharge were used to inform the models. The best-performing model for each study outcome (the PRAISE score) was tested in an external validation cohort of 3444 patients with ACS pooled from a randomised controlled trial and three prospective registries. Model performance was assessed according to a range of learning metrics including area under the receiver operating characteristic curve (AUC). FINDINGS: The PRAISE score showed an AUC of 0·82 (95% CI 0·78-0·85) in the internal validation cohort and 0·92 (0·90-0·93) in the external validation cohort for 1-year all-cause death; an AUC of 0·74 (0·70-0·78) in the internal validation cohort and 0·81 (0·76-0·85) in the external validation cohort for 1-year myocardial infarction; and an AUC of 0·70 (0·66-0·75) in the internal validation cohort and 0·86 (0·82-0·89) in the external validation cohort for 1-year major bleeding. INTERPRETATION: A machine learning-based approach for the identification of predictors of events after an ACS is feasible and effective. The PRAISE score showed accurate discriminative capabilities for the prediction of all-cause death, myocardial infarction, and major bleeding, and might be useful to guide clinical decision making. FUNDING: None.
Authors: Ziad Faramand; Mohammad Alrawashdeh; Stephanie Helman; Zeineb Bouzid; Christian Martin-Gill; Clifton Callaway; Salah Al-Zaiti Journal: Res Nurs Health Date: 2021-11-24 Impact factor: 2.228
Authors: Jacopo Burrello; Guglielmo Gallone; Alessio Burrello; Daniele Jahier Pagliari; Eline H Ploumen; Mario Iannaccone; Leonardo De Luca; Paolo Zocca; Giuseppe Patti; Enrico Cerrato; Wojciech Wojakowski; Giuseppe Venuti; Ovidio De Filippo; Alessio Mattesini; Nicola Ryan; Gérard Helft; Saverio Muscoli; Jing Kan; Imad Sheiban; Radoslaw Parma; Daniela Trabattoni; Massimo Giammaria; Alessandra Truffa; Francesco Piroli; Yoichi Imori; Bernardo Cortese; Pierluigi Omedè; Federico Conrotto; Shao-Liang Chen; Javier Escaned; Rosaly A Buiten; Clemens Von Birgelen; Paolo Mulatero; Gaetano Maria De Ferrari; Silvia Monticone; Fabrizio D'Ascenzo Journal: J Pers Med Date: 2022-06-17
Authors: Sebastian Weichwald; Alessandro Candreva; Rebekka Burkholz; Roland Klingenberg; Lorenz Räber; Dik Heg; Robert Manka; Baris Gencer; François Mach; David Nanchen; Nicolas Rodondi; Stephan Windecker; Reijo Laaksonen; Stanley L Hazen; Arnold von Eckardstein; Frank Ruschitzka; Thomas F Lüscher; Joachim M Buhmann; Christian M Matter Journal: Eur Heart J Acute Cardiovasc Care Date: 2021-10-27
Authors: Richard John Woodman; Kimberley Bryant; Michael J Sorich; Alberto Pilotto; Arduino Aleksander Mangoni Journal: J Med Internet Res Date: 2021-06-21 Impact factor: 5.428