Tsao CW, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, et al. Heart disease and stroke statistics—2023 update: a report from the American Heart Association. Circulation. 2023;147(8):e93–621.
Heidenreich PA, Trogdon JG, Khavjou OA, Butler J, Dracup K, Ezekowitz MD, et al. Forecasting the future of cardiovascular disease in the United States. Circulation. 2011;123(8):933–44.
Kindig D, Stoddart G. What is population health? Am J Public Health. 2003;93(3):380–3.
Article PubMed PubMed Central Google Scholar
Kannel WB, McGee D, Gordon T. A general cardiovascular risk profile: the Framingham Study. Am J Cardiol. 1976;38(1):46–51.
Article CAS PubMed Google Scholar
Goff D, Lloyd-Jones D, Bennett G. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol 2013 Nov 12 [E-pub ahead of print. J Am Coll Cardiol. 2014;63(25).
Khera R, Pandey A, Ayers CR, Carnethon MR, Greenland P, Ndumele CE, et al. Performance of the pooled cohort equations to estimate atherosclerotic cardiovascular disease risk by body mass index. JAMA Network Open. 2020;3(10):e2023242.
Article PubMed PubMed Central Google Scholar
Chia YC, Gray SYW, Ching SM, Lim HM, Chinna K. Validation of the Framingham general cardiovascular risk score in a multiethnic Asian population: a retrospective cohort study. BMJ Open. 2015;5(5):e007324.
Article PubMed PubMed Central Google Scholar
Brindle PM, McConnachie A, Upton MN, Hart CL, Davey Smith G, Watt GC. The accuracy of the Framingham risk-score in different socioeconomic groups: a prospective study. Br J Gen Pract. 2005;55(520):838–45.
PubMed PubMed Central Google Scholar
Bzdok D, Altman N, Krzywinski M. Statistics versus machine learning. Nat Methods. 2018;15(4):233–4.
Article CAS PubMed PubMed Central Google Scholar
Gautam N, Ghanta SN, Clausen A, Saluja P, Sivakumar K, Dhar G, et al. Contemporary applications of machine learning for device therapy in heart failure. JACC: Heart Failure. 2022;10(9):603–22.
Dobrev D. A definition of artificial intelligence. arXiv preprint arXiv:12101568. 2012.
Gautam N, Saluja P, Malkawi A, Rabbat MG, Al-Mallah MH, Pontone G, et al. Current and future applications of artificial intelligence in coronary artery disease. Healthcare. 2022;10(2):232.
Article PubMed PubMed Central Google Scholar
Bizopoulos P, Koutsouris D. Deep learning in cardiology. IEEE Rev Biomed Eng. 2019;12:168–93.
Grundy SM, Stone NJ, Bailey AL, Beam C, Birtcher KK, Blumenthal RS, et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA guideline on the management of blood cholesterol: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol. 2019;73(24):3168–209.
Reboussin DM, Allen NB, Griswold ME, Guallar E, Hong Y, Lackland DT, et al. Systematic review for the 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA guideline for the prevention, detection, evaluation, and management of high blood pressure in adults: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Hypertension. 2018;71(6):e116–35.
Article CAS PubMed Google Scholar
D’Agostino RB Sr, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743–53.
Goff DC Jr, Lloyd-Jones DM, Bennett G, Coady S, D’agostino RB, Gibbons R, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American college of cardiology/American heart association task force on practice guidelines. Circulation. 2014;129(25 suppl 2):S49–73.
Ridker PM, Cook NR. Statins: new American guidelines for prevention of cardiovascular disease. The Lancet. 2013;382(9907):1762–5.
Rodriguez F, Chung S, Blum MR, Coulet A, Basu S, Palaniappan LP. Atherosclerotic cardiovascular disease risk prediction in disaggregated Asian and Hispanic subgroups using electronic health records. J Am Heart Assoc. 2019;8(14):e011874.
Article PubMed PubMed Central Google Scholar
Cho S-Y, Kim S-H, Kang S-H, Lee KJ, Choi D, Kang S, et al. Pre-existing and machine learning-based models for cardiovascular risk prediction. Sci Rep. 2021;11(1):8886.
Article CAS PubMed PubMed Central Google Scholar
Motwani M, Dey D, Berman DS, Germano G, Achenbach S, Al-Mallah MH, et al. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. Eur Heart J. 2016;38(7):500–7.
Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS One. 2017;12(4):e0174944.
Article PubMed PubMed Central Google Scholar
Dimopoulos AC, Nikolaidou M, Caballero FF, Engchuan W, Sanchez-Niubo A, Arndt H, et al. Machine learning methodologies versus cardiovascular risk scores, in predicting disease risk. BMC Med Res Methodol. 2018;18(1):179.
Article PubMed PubMed Central Google Scholar
Nakanishi R, Slomka PJ, Rios R, Betancur J, Blaha MJ, Nasir K, et al. Machine learning adds to clinical and CAC assessments in predicting 10-year CHD and CVD deaths. JACC Cardiovasc Imaging. 2021;14(3):615–25.
Sarraju A, Ward A, Chung S, Li J, Scheinker D, Rodríguez F. Machine learning approaches improve risk stratification for secondary cardiovascular disease prevention in multiethnic patients. Open Heart. 2021;8(2).
Rousset A, Dellamonica D, Menuet R, Lira Pineda A, Sabatine MS, Giugliano RP, et al. Can machine learning bring cardiovascular risk assessment to the next level? A methodological study using FOURIER trial data. Eur Heart J-Digit Health. 2021;3(1):38–48.
Article PubMed PubMed Central Google Scholar
He F, Page JH, Tandi J, Ghosh A, Liman C, Sarkar J, et al. Major adverse cardiovascular event risk prediction in Asian patients after myocardial infarction: a novel, dynamic, machine-learning approach. J Asian Pac Soc Cardiol. 2023;2(e25):2023.
•• Forrest IS, Petrazzini BO, Duffy Á, Park JK, Marquez-Luna C, Jordan DM, et al. Machine learning-based marker for coronary artery disease: derivation and validation in two longitudinal cohorts. The Lancet. 2023;401(10372):215–25. Findings from this study introduce the idea of viewing CAD on a continuum, with scores developed to predict the risk, and the clinical progression of the disease.
Bild DE, Bluemke DA, Burke GL, Detrano R, Diez Roux AV, Folsom AR, et al. Multi-ethnic study of atherosclerosis: objectives and design. Am J Epidemiol. 2002;156(9):871–81.
Kakadiaris IA, Vrigkas M, Yen AA, Kuznetsova T, Budoff M, Naghavi M. Machine learning outperforms ACC / AHA CVD risk calculator in MESA. J Am Heart Assoc. 2018;7(22):e009476.
Article PubMed PubMed Central Google Scholar
Folsom AR, Chambless LE, Ballantyne CM, Coresh J, Heiss G, Wu KK, et al. An assessment of incremental coronary risk prediction using C-reactive protein and other novel risk markers: the atherosclerosis risk in communities study. Arch Intern Med. 2006;166(13):1368–73.
Article CAS PubMed Google Scholar
Rana JS, Gransar H, Wong ND, Shaw L, Pencina M, Nasir K, et al. Comparative value of coronary artery calcium and multiple blood biomarkers for prognostication of cardiovascular events. Am J Cardiol. 2012;109(10):1449–53.
Article CAS PubMed Google Scholar
Wang TJ, Gona P, Larson MG, Tofler GH, Levy D, Newton-Cheh C, et al. Multiple biomarkers for the prediction of first major cardiovascular events and death. N Engl J Med. 2006;355(25):2631–9.
Article CAS PubMed Google Scholar
• Tamarappoo BK, Lin A, Commandeur F, McElhinney PA, Cadet S, Goeller M, et al. Machine learning integration of circulating and imaging biomarkers for explainable patient-specific prediction of cardiac events: a prospective study. Atherosclerosis. 2021;318:76–82. Findings from this study demonstrate the added benefit of biomarkers when used with clinical imaging parameters for cardiovascular risk prediction, with the use of machine learning.
Lin S, Li Z, Fu B, Chen S, Li X, Wang Y, et al. Feasibility of using deep learning to detect coronary artery disease based on facial photo. Eur Heart J. 2020;41(46):4400–11.
Rim TH, Lee CJ, Tham YC, Cheung N, Yu M, Lee G, et al. Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs. Lancet Digit Health. 2021;3(5):e306–16.
Article CAS PubMed Google Scholar
Guo Y, Xia C, Zhong Y, Wei Y, Zhu H, Ma J, et al. Machine learning-enhanced echocardiography for screening coronary artery disease. Biomed Eng Online. 2023;22(1):44.
Article PubMed PubMed Central Google Scholar
•• Al'Aref SJ, Maliakal G, Singh G, van Rosendael AR, Ma X, Xu Z, et al. Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry. Eur Heart J. 2020;41(3):359–67. Findings from this study show a superior prediction power of imaging variables when used in conjunction with machine learning when compared to clinical risk prediction scores for the prediction of CAD.
Al’Aref SJ, Maliakal G, Singh G, van Rosendael AR, Ma X, Xu Z, et al. Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry. Eur Heart J. 2020;41(3):359–67.
Khav N, Ihdayhid AR, Ko B. CT-derived fractional flow reserve (CT-FFR) in the evaluation of coronary artery disease. Heart Lung Circ. 2020;29(11):1621–32.
Yang S, Koo BK, Hoshino M, Lee JM, Murai T, Park J, et al. CT angiographic and plaque predictors of functionally significant coronary disease and outcome using machine learning. JACC Cardiovasc Imaging. 2021;14(3):629–41.
Gillman MW, Hammond RA. Precision treatment and precision prevention: integrating “below and above the skin.” JAMA Pediatr. 2016;170(1):9–10.
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