If one could better predict which individuals were likely to suffer cardiovascular disease (CVD) events (e.g. by measuring additional predictive risk factors), then preventive interventions (lifestyle or pharmacological) could be better targeted based on level of predicted risk. Such advances would have benefits in terms of both public health and health care resource use, and are complementary to population-level interventions related for example to diet and exercise.
There are a number of ways in which the value of measuring additional risk factors to enhance CVD risk prediction can be evaluated. The first is in terms of standard measures of risk discrimination or prediction accuracy, such as the C-index, that assesses the concordance between predicted risk and observed CVD events; a second is in terms of modelling the additional number of CVD events that might be avoided, for example if those over a certain level of predicted risk are treated with a statin; a third is in terms of the gains in life expectancy that could be achieved; and fourth is a fuller health economic assessment in terms of life expectancy, quality of life including side effects due to treatment, and the additional costs of measurements and treatments.
The CEU has been in the vanguard of developing and applying risk prediction methods in the context of meta-analysis of individual participant data (IPD) from multiple prospective studies [1-5]. We have evaluated the incremental predictive value of several emerging CVD risk markers [6-9] and conducted public health modelling to quantify the impact of diabetes and cardiometabolic multimorbidity on life expectancy [10-11]. As yet, we have not performed fuller health economic analyses, although we have reviewed the methods used by others based on multistate transition models , and have developed these further for forthcoming application.
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- Covariate-adjusted measures of discrimination for survival data. Biom J. 2015 Jul;57(4):592-613. [PubMed]
- Derivation and assessment of risk prediction models using case-cohort data. BMC Med Res Methodol. 2013 Sep 13;13:113. [PubMed]
- A framework for quantifying net benefits of alternative prognostic models. Stat Med. 2012;31:114-130. [PubMed]
- Measures to assess the prognostic ability of the stratified Cox proportional hazards model. Stat Med. 2009 Feb 1;28(3):389-411. [PubMed]
- C-reactive protein, fibrinogen, and cardiovascular risk. N Engl J Med. 2012 Oct 4;367(14):1310-20 [PubMed]
- Major lipids, apolipoproteins, and risk of vascular disease. 2009; 302 (18):1993-2000. [PubMed]
- Glycated hemoglobin measurement and prediction of cardiovascular disease. JAMA. 2014 Mar 26;311(12):1225-33 [PubMed]
- Natriuretic peptides and integrated risk assessment for cardiovascular disease: an individual-participant-data meta-analysis. Lancet Diabetes Endocrinol. 2016 Oct;4(10):840-9. [PubMed]
- Diabetes mellitus, fasting glucose, and risk of cause-specific death. N Engl J Med. 2011;364:829-841. [PubMed]
- Association of Cardiometabolic Multimorbidity With Mortality. 2015 Jul 7;314(1):52-60. [PubMed]
- Modeling the costs and long-term health benefits of screening the general population for risks of cardiovascular disease: a review of methods used in the literature. Eur J Health Econ. 2016 Nov;17(8):1041-1053 [PubMed]