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 19826 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.

Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets

D'Ascenzo F.
First
;
De Filippo O.;Gallone G.;Mittone G.;Quadri G.;Aldinucci M.;De Ferrari G. M.
Last
;
Piroli F.;Saglietto A.;Conrotto F.;Pennone M.;Bocchino P. P.;Sperti M.;Rognoni A.;Biondi Zoccai G.;Colonnelli I.;Cantalupo B.;Esposito R.;Leonardi S.;Grosso Marra W.;Malavolta M.;Arfat Y.
2021

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 19826 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.
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https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)32519-8/
D'Ascenzo F.; De Filippo O.; Gallone G.; Mittone G.; Deriu M.A.; Iannaccone M.; Ariza-Sole A.; Liebetrau C.; Manzano-Fernandez S.; Quadri G.; Kinnaird T.; Campo G.; Simao Henriques J.P.; Hughes J.M.; Dominguez-Rodriguez A.; Aldinucci M.; Morbiducci U.; Patti G.; Raposeiras-Roubin S.; Abu-Assi E.; De Ferrari G.M.; Piroli F.; Saglietto A.; Conrotto F.; Omede P.; Montefusco A.; Pennone M.; Bruno F.; Bocchino P.P.; Boccuzzi G.; Cerrato E.; Varbella F.; Sperti M.; Wilton S.B.; Velicki L.; Xanthopoulou I.; Cequier A.; Iniguez-Romo A.; Munoz Pousa I.; Cespon Fernandez M.; Caneiro Queija B.; Cobas-Paz R.; Lopez-Cuenca A.; Garay A.; Blanco P.F.; Rognoni A.; Biondi Zoccai G.; Biscaglia S.; Nunez-Gil I.; Fujii T.; Durante A.; Song X.; Kawaji T.; Alexopoulos D.; Huczek Z.; Gonzalez Juanatey J.R.; Nie S.-P.; Kawashiri M.-A.; Colonnelli I.; Cantalupo B.; Esposito R.; Leonardi S.; Grosso Marra W.; Chieffo A.; Michelucci U.; Piga D.; Malavolta M.; Gili S.; Mennuni M.; Montalto C.; Oltrona Visconti L.; Arfat Y.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/1767072
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