Machine Learning Outperforms ACC / AHA CVD Risk Calculator in MESA.
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, therefor...
| Publicado en: | Journal of the American Heart Association Vol. 7; no. 22; pp. 1 - 37 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Wiley-Blackwell
11/20/2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=133263822&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 133263822 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20479980 FSQ9 jtl: Journal of the American Heart Association issn: 20479980 maglogo: Y pubinfo: dt: 11/20/2018 vid: 7 iid: 22 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 133263822 133263822 NLM30571498 10.1161/JAHA.118.009476 NLM30571498 133263822 ppf: 1 ppct: 36 formats: tig: atl: Machine Learning Outperforms ACC / AHA CVD Risk Calculator in MESA. aug: au: Kakadiaris, Ioannis A. Vrigkas, Michalis Yen, Albert A. Kuznetsova, Tatiana Budoff, Matthew Naghavi, Morteza affil: Computational Biomedicine Lab, University of Houston, TX sug: subj: Cardiovascular Diseases Diagnosis Risk Assessment Methods Sensitivity and Specificity Cardiovascular Diseases Prevention and Control Coronary Arteriosclerosis Diagnosis Coronary Arteriosclerosis Etiology Coronary Arteriosclerosis Prevention and Control Aged Risk Factors Male Cardiovascular Diseases Etiology Middle Age Antilipemic Agents Therapeutic Use Female Short Portable Mental Status Questionnaire Aged: 65+ years Middle Aged: 45-64 years Male Female ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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