Review on Cardiovascular Risk Prediction

Thilanga Ruwanpathirana, Alice Owen, Christopher M. Reid

Research output: Contribution to journalArticleResearchpeer-review

29 Citations (Scopus)


Summary: The objectives were to review the currently available and widely used cardiovascular risk assessment models and to examine the evidence available on new biomarkers and the nonclinical measures in improving the risk prediction in the population level. Identification of individuals at risk of cardiovascular disease (CVD), to better target prevention and treatment, has become a top research priority. Cardiovascular risk prediction has progressed with the development and refinement of risk prediction models based upon established clinical factors, and the discovery of novel biomarkers, lifestyle, and social factors may offer additional information on the risk of disease. However, a significant proportion of individuals who have a myocardial infarction still are categorized as low risk by many of the available methods. Although novel biomarkers can improve risk prediction, including B-type natriuretic peptides which have shown the best predictive capacity per unit cost, there is concern that the use of risk prediction strategies which rely upon new/or expensive biomarkers could further broaden social inequalities in CVD. In contrast, nonclinical factors such as work stress, social isolation, and early childhood experience also appear to be associated with cardiovascular risk and have the potential to be utilized for the baseline risk stratification at the population level. A stepwise approach of nonclinical methods followed by risk scores consisting of clinical risk factors may offer a better option for initial and subsequent screening, preserving more specialized approaches including novel biomarkers for enhanced risk stratification at population level in a cost-effective manner.

Original languageEnglish
Pages (from-to)62-70
Number of pages9
JournalCardiovascular Therapeutics
Issue number2
Publication statusPublished - 1 Apr 2015


  • Cardiovascular disease
  • Cost-effectiveness
  • Novel biomarkers
  • Risk prediction models

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