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Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study
Fabrizio D'Ascenzo1, Ovidio De Filippo1, Guglielmo Gallone1
1Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza, Turin, Italy; Cardiology, Department of Medical Sciences, University of Turin, Turin, Italy.
Insights
A new machine learning model, the PRAISE score, accurately predicts death, heart attack, and bleeding events in acute coronary syndrome (ACS) patients. This tool aids in personalized management strategies post-ACS.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Informatics
Background:
- Current prediction tools for ischemic and bleeding events post-acute coronary syndrome (ACS) lack sufficient accuracy for personalized patient management.
- There is a need for improved risk stratification models to guide clinical decisions in ACS patients.
Purpose of the Study:
- To develop and validate a machine learning-based risk stratification model to predict adverse events after ACS.
- To assess the model's performance in predicting all-cause death, recurrent myocardial infarction, and major bleeding.
Main Methods:
- Trained machine learning models on a large cohort (19,826 patients) from BleeMACS and RENAMI registries.
- Utilized 25 routinely assessed clinical features at discharge to inform the models.
- Validated the best-performing model, the PRAISE score, in an external cohort (3444 patients).
Main Results:
- The PRAISE score demonstrated strong discriminative capabilities across validation cohorts.
- Achieved an Area Under the Curve (AUC) of 0.82 (internal) and 0.92 (external) for predicting 1-year all-cause death.
- Showed AUCs of 0.74/0.81 for myocardial infarction and 0.70/0.86 for major bleeding in internal/external validation, respectively.
Conclusions:
- Machine learning offers a feasible and effective approach for predicting post-ACS events.
- The PRAISE score exhibits accurate discriminative ability for key adverse outcomes.
- The PRAISE score may serve as a valuable tool to guide clinical decision-making in ACS management.
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.
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