1. Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets.
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D'Ascenzo, Fabrizio, De Filippo, Ovidio, Gallone, Guglielmo, Mittone, Gianluca, Deriu, Marco Agostino, Iannaccone, Mario, Ariza-Solé, Albert, Liebetrau, Christoph, Manzano-Fernández, Sergio, Quadri, Giorgio, Kinnaird, Tim, Campo, Gianluca, Simao Henriques, Jose Paulo, Hughes, James M, Dominguez-Rodriguez, Alberto, Aldinucci, Marco, Morbiducci, Umberto, Patti, Giuseppe, Raposeiras-Roubin, Sergio, and Abu-Assi, Emad
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ACUTE coronary syndrome , *MYOCARDIAL infarction , *RECEIVER operating characteristic curves , *RANDOMIZED controlled trials , *PRAISE , *MORTALITY , *SURGICAL complications , *ACQUISITION of data , *HEMORRHAGE , *DISEASE complications - 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. [ABSTRACT FROM AUTHOR]- Published
- 2021
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