Machine learning outperforms ACC/AHA CVD risk calculator in MESA

Ioannis A. Kakadiaris, Michalis Vrigkas, Albert A. Yen, Tatiana Kuznetsova, Matthew Budoff, Morteza Naghavi

Research output: Contribution to journalArticlepeer-review

141 Scopus citations

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 (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 shortterm CVD risk prediction.

Original languageEnglish (US)
Article numbere009476
JournalJournal of the American Heart Association
Volume7
Issue number22
DOIs
StatePublished - Nov 1 2018

Keywords

  • Artificial intelligence
  • Atherosclerosis
  • Cardiovascular disease prevention
  • Cardiovascular disease risk factors
  • Cardiovascular risk
  • Clinical decision support
  • Machine learning
  • Prediction statistics
  • Statin

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

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