Using machine learning to predict acute myocardial infarction and ischemic heart disease in primary care
N Salet1, A Gökdemir1,2, J Preijde2
1Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, The Netherlands.
Insights
Machine learning models significantly outperform the SMART algorithm in predicting acute myocardial infarction and ischemic heart disease in primary care. These advanced ML tools offer potential for improved cardiovascular disease prediction and personalized patient care.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Cardiovascular disease (CVD) necessitates early detection, ideally in primary care settings.
- Predicting acute myocardial infarction (AMI) and ischemic heart disease (IHD) is crucial for effective CVD management.
- Machine learning (ML) offers a novel approach to enhance CVD risk prediction in primary care.
Purpose of the Study:
- To develop and evaluate ML models for predicting AMI and IHD in primary care patients.
- To compare the performance of ML models against the established SMART risk prediction algorithm.
- To identify key predictors contributing to ML model accuracy for CVD risk.
Main Methods:
- Utilized patient-level medical record data (n=13,218) from 90 GP practices (2011-2021).
- Constructed two random forest ML models (AMI and IHD) and a linear SMART algorithm model.
- Employed temporal cross-validation for performance assessment and identified key predictive features.
Main Results:
- ML models demonstrated superior performance over the SMART algorithm across all metrics.
- The AMI prediction model achieved an accuracy of 0.97, AUC of 0.96, and Brier score of 0.03.
- Key predictors included anticoagulants/antiplatelet use, systolic blood pressure, mean blood glucose, and eGFR.
Conclusions:
- ML holds significant potential for improving CVD prediction accuracy in primary care.
- ML models can support individualized risk assessments, aiding primary care physicians in prevention strategies.
- While effective, the interpretability of ML model predictors requires further consideration for clinical integration.
Background:
Early recognition, which preferably happens in primary care, is the most important tool to combat cardiovascular disease (CVD). This study aims to predict acute myocardial infarction (AMI) and ischemic heart disease (IHD) using Machine Learning (ML) in primary care cardiovascular patients. We compare the ML-models' performance with that of the common SMART algorithm and discuss clinical implications.
Methods And Results:
Patient-level medical record data (n = 13,218) collected between 2011-2021 from 90 GP-practices were used to construct two random forest models (one for AMI and one for IHD) as well as a linear model based on the SMART risk prediction algorithm as a suitable comparator. The data contained patient-level predictors, including demographics, procedures, medications, biometrics, and diagnosis. Temporal cross-validation was used to assess performance. Furthermore, predictors that contributed most to the ML-models' accuracy were identified. The ML-model predicting AMI had an accuracy of 0.97, a sensitivity of 0.67, a specificity of 1.00 and a precision of 0.99. The AUC was 0.96 and the Brier score was 0.03. The IHD-model had similar performance. In both ML-models anticoagulants/antiplatelet use, systolic blood pressure, mean blood glucose, and eGFR contributed most to model accuracy. For both outcomes, the SMART algorithm was substantially outperformed by ML on all metrics.
Conclusion:
Our findings underline the potential of using ML for CVD prediction purposes in primary care, although the interpretation of predictors can be difficult. Clinicians, patients, and researchers might benefit from transitioning to using ML-models in support of individualized predictions by primary care physicians and subsequent (secondary) prevention.
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