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Multivariable Evaluation of Candidates for Cardiovascular Disease

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Essential Cardiology

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Abstract

A preventive approach to the management of atherosclerotic cardiovascular disease (CVD) is needed. It is the leading cause of death in the USA and across most of the world. Those fortunate enough to survive can seldom be restored to full function. Extensive epidemiologic research and clinical trials have identified modifiable predisposing risk factors which, when corrected, can reduce the likelihood of its occurrence. Because CVD is a multifactorial disease with the risk factors interacting multiplicatively over time to promote it, these risk factors need to be assessed jointly. To accomplish this, multivariate risk prediction functions (algorithms) which estimate the probability of cardiovascular events conditional on the burden of specified risk factors have been produced to facilitate evaluation of candidates for CVD in need of preventive management.

Over six decades, the Framingham Study and other epidemiological studies have identified modifiable CVD risk factors that have a strong dose-dependent and independent relationship to the rate of development of atherosclerotic CVD. They include classes of risk factors such as atherogenic personal traits, lifestyles that promote them, and innate susceptibility. Most of the relevant risk factors are easy to assess during an office visit and include systolic blood pressure, blood lipids (total and HDL cholesterol) diabetes status, and current smoking. These risk factors in addition to age and sex are the standard CVD risk factors that are basic components in most risk prediction functions.

We summarize the data that established the standard risk factors. We then present the justification and need for multivariate evaluation and prediction functions along with some history. We illustrate the above using examples of existing functions. Then we discuss the evaluation of the performance of the functions and the validity and transportability of existing functions. We end with the discussion of adding new variables (novel biomarkers) to risk prediction.

†deceased

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Recommended Reading

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    Google Scholar 

  • D’Agostino RB, Grundy S, Sullivan L, Wilson P. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic group investigation. JAMA. 2001;286:180–7.

    Article  PubMed  Google Scholar 

  • D’Agostino Sr RB, 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;117:743–53.

    Article  PubMed  Google Scholar 

  • Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA. 2001;285:2486–97.

    Article  Google Scholar 

  • Folsom AR, Chambless LE, Ballantyne CM, Coresh J, Heiss J, 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;166:1368–73.

    Article  PubMed  CAS  Google Scholar 

  • Pencina MJ, D’Agostino RB. Overall C as a measure of discrimination in survival analysis; model specific population value and confidence interval estimations. Stat Med. 2004;23:109023.

    Article  Google Scholar 

  • Pencina MJ, D’Agostino Sr RB, D’Agostino Jr RB, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27:207–12.

    Article  Google Scholar 

  • Wilson PWF, D’Agostino RB, Levy D, Belanger AM, Slibershatz H, Kannel WB, et al. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97:1837–47.

    Article  PubMed  CAS  Google Scholar 

  • Wong TJ, Gona P, Larson MG, Tofler GH, Levy D, Newton-Chen C, et al. Multiple biomarkers for the prediction of first major cardiovascular events and death. N Engl J Med. 2006;355:2631–9.

    Article  Google Scholar 

  • Zachariah J, Vasan RA, D’Agostino RB. The burden of increasing worldwide cardiovascular disease. In: Fuster V, Walsh R, Harrington R, editors. Hurst’s the heart. New York: McGraw-Hill; 2010.

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Authors and Affiliations

  1. Department of Mathematics and Statistics, Boston University, 111 Cummington Street, Boston, MA, 02215, USA

    Ralph B. D’Agostino Sr. FAHA

  2. Department of Medicine and Public Health, Framingham Study/Boston University School of Medicine, Framingham, MA, USA

    William B. Kannel MD, MPH, FACC

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  1. Ralph B. D’Agostino Sr. FAHA
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  2. William B. Kannel MD, MPH, FACC
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Correspondence to Ralph B. D’Agostino Sr. FAHA .

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  1. The Mount Sinai School of Medicine, The James J. Peters VA Medical Center, Department of Medicine, West Kingsbridge Rd 130, Bronx, 10468, New York, USA

    Clive Rosendorff

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D’Agostino, R.B., Kannel, W.B. (2013). Multivariable Evaluation of Candidates for Cardiovascular Disease. In: Rosendorff, C. (eds) Essential Cardiology. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6705-2_1

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  • DOI: https://doi.org/10.1007/978-1-4614-6705-2_1

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