Machine Learning Outperforms ACC/AHA CVD Risk Calculator in MESA

Autor: Ioannis A. Kakadiaris, Michalis Vrigkas, Albert A. Yen, Tatiana Kuznetsova, Matthew Budoff, Morteza Naghavi
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Zdroj: Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, Vol 7, Iss 22 (2018)
Druh dokumentu: article
ISSN: 2047-9980
40662314
DOI: 10.1161/JAHA.118.009476
Popis: 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 (SVMs) 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.
Databáze: Directory of Open Access Journals
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