Can machine learning bring cardiovascular risk assessment to the next level? A methodological study using FOURIER trial data

Abstract

Aims: Through this proof of concept, we studied the potential added value of machine learning (ML) methods in building cardiovascular risk scores from structured data and the conditions under which they outperform linear statistical models. Methods and results: -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 ML 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, ML methods could open the door to more powerful tools for patient risk stratification and treatment allocation strategies.

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