Machine Learning Outperforms ACC/AHA CVD Risk Calculator in MESA

Abstract

Background Studies have demonstrated that the current US guidelines based on American College of Cardiology/American Heart Association (ACC/AHA) Pooled Cohort Equations Risk Calculator may underestimate risk of atherosclerotic cardiovascular disease ( CVD ) in certain high‐risk individuals, therefore missing opportunities for intensive therapy and preventing CVD events. Similarly, the guidelines may overestimate risk in low risk populations resulting in unnecessary statin therapy. We used Machine Learning ( ML ) to tackle this problem. Methods and Results We developed a ML Risk Calculator based on Support Vector Machines ( SVM s) using a 13‐year follow up data set from MESA (the Multi‐Ethnic Study of Atherosclerosis) of 6459 participants who were atherosclerotic CVD‐free at baseline. We provided identical input to both risk calculators and compared their performance. We then used the FLEMENGHO study (the Flemish Study of Environment, Genes and Health Outcomes) to validate the model in an external cohort. ACC / AHA Risk Calculator, based on 7.5% 10‐year risk threshold, recommended statin to 46.0%. Despite this high proportion, 23.8% of the 480 “Hard CVD ” events occurred in those not recommended statin, resulting in sensitivity 0.76, specificity 0.56, and AUC 0.71. In contrast, ML Risk Calculator recommended only 11.4% to take statin, and only 14.4% of “Hard CVD ” events occurred in those not recommended statin, resulting in sensitivity 0.86, specificity 0.95, and AUC 0.92. Similar results were found for prediction of “All CVD ” events. Conclusions The ML Risk Calculator outperformed the ACC/AHA Risk Calculator by recommending less drug therapy, yet missing fewer events. Additional studies are underway to validate the ML model in other cohorts and to explore its ability in short‐term CVD risk prediction.

Machine Learning Algorithms for Predicting Coronary Artery Disease: Efforts Toward an Open Source Solution

“…Traditional approaches assess these risk factors to predict future risk (prognosis) of cardiovascular disease [11]. However, a large number of individuals at risk of cardiovascular disease fail to be identified by these approaches, while some individuals not at risk are given preventive treatment unnecessarily (see [12,13]). Several of the ML algorithms have the ability to summarize the impact of individual variables on response variable and are referred as "variables of importance", thus leading to the building of prognostic models [14].…”

Artificial Intelligence, Machine Learning, and Cardiovascular Disease

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“…[26][27][28][29][30][31] • AI-based systems can help in improving health care outcomes and systems based practice. 43,50 Abbreviations: AI, artificial intelligence; CAD, coronary artery disease; CT, computed tomography; MRI, magnetic resonance imaging.…”

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