Can machine learning bring cardiovascular risk assessment to the next level?

Abstract Objective Through this proof of concept, we studied the potential added value of machine learning methods in building cardiovascular risk scores from structured data and the conditions under which they outperform linear statistical models. Methods Relying on extensive cardiovascular clinical data from FOURIER, a randomized clinical trial to test for evolocumab efficacy, we compared linear models, neural networks, random forest, and gradient boosting machines for predicting the risk of major adverse cardiovascular events. To study the relative strengths of each method, we extended the comparison to restricted subsets of the full FOURIER dataset, limiting either the number of available patients or the number of their characteristics. Results When using all the 428 covariates available in the dataset, machine learning methods significantly (c-index 0.67, p-value 2e-5) outperformed linear models built from the same variables (c-index 0.62), as well as a reference cardiovascular risk score based on only 10 variables (c-index 0.60). We showed that gradient boosting—the best performing model in our setting—requires fewer patients and significantly outperforms linear models when using large numbers of variables. On the other hand, we illustrate how linear models suffer from being trained on too many variables, thus requiring a more careful prior selection. These machine learning methods proved to consistently improve risk assessment, to be interpretable despite their complexity and to help identify the minimal set of covariates necessary to achieve top performance. Conclusion In the field of secondary cardiovascular events prevention, given the increased availability of extensive electronic health records, machine learning methods could open the door to more powerful tools for patient risk stratification and treatment allocation strategies.