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.
2021-01-01

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.
2021
397
199
207
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: https://hdl.handle.net/2318/1767072
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