作者
Fabrizio D'Ascenzo, Ovidio De Filippo, Guglielmo Gallone, Gianluca Mittone, Marco Agostino Deriu, Mario Iannaccone, Albert Ariza-Solé, Christoph Liebetrau, Sergio Manzano-Fernández, Giorgio Quadri, Tim Kinnaird, Gianluca Campo, Jose Paulo Simao Henriques, James M Hughes, Alberto Dominguez-Rodriguez, Marco Aldinucci, Umberto Morbiducci, Giuseppe Patti, Sergio Raposeiras-Roubin, Emad Abu-Assi, Gaetano Maria De Ferrari, Francesco Piroli, Andrea Saglietto, Federico Conrotto, Pierluigi Omedé, Antonio Montefusco, Mauro Pennone, Francesco Bruno, Pier Paolo Bocchino, Giacomo Boccuzzi, Enrico Cerrato, Ferdinando Varbella, Michela Sperti, Stephen B Wilton, Lazar Velicki, Ioanna Xanthopoulou, Angel Cequier, Andres Iniguez-Romo, Isabel Munoz Pousa, Maria Cespon Fernandez, Berenice Caneiro Queija, Rafael Cobas-Paz, Angel Lopez-Cuenca, Alberto Garay, Pedro Flores Blanco, Andrea Rognoni, Giuseppe Biondi Zoccai, Simone Biscaglia, Ivan Nunez-Gil, Toshiharu Fujii, Alessandro Durante, Xiantao Song, Tetsuma Kawaji, Dimitrios Alexopoulos, Zenon Huczek, Jose Ramon Gonzalez Juanatey, Shao-Ping Nie, Masa-aki Kawashiri, Iacopo Colonnelli, Barbara Cantalupo, Roberto Esposito, Sergio Leonardi, Walter Grosso Marra, Alaide Chieffo, Umberto Michelucci, Dario Piga, Marta Malavolta, Sebastiano Gili, Marco Mennuni, Claudio Montalto, Luigi Oltrona Visconti, Yasir Arfat
发表日期
2021/1/16
期刊
The Lancet
卷号
397
期号
10270
页码范围
199-207
出版商
Elsevier
简介
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 …
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F D'Ascenzo, O De Filippo, G Gallone, G Mittone… - The Lancet, 2021