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Review on cardiovascular risk prediction.

Thilanga Ruwanpathirana1, Alice Owen, Christopher M Reid

  • 1CCRE Therapeutics, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Vic., Australia.

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PubMed
Summary

Identifying cardiovascular disease (CVD) risk is crucial. Novel nonclinical factors and biomarkers can improve risk prediction, but a stepwise approach using nonclinical methods first may be most cost-effective for population-level screening.

Keywords:
Cardiovascular diseaseCost-effectivenessNovel biomarkersRisk prediction models

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Area of Science:

  • Cardiology
  • Public Health
  • Biomarkers

Background:

  • Cardiovascular disease (CVD) risk prediction is a priority for targeted prevention.
  • Current models using clinical factors have limitations, often misclassifying high-risk individuals.
  • Novel biomarkers and nonclinical factors offer potential for improved risk assessment.

Purpose of the Study:

  • To review existing cardiovascular risk assessment models.
  • To examine evidence on new biomarkers for risk prediction.
  • To evaluate nonclinical measures for improving population-level CVD risk stratification.

Main Methods:

  • Literature review of cardiovascular risk assessment models.
  • Analysis of evidence for novel biomarkers (e.g., B-type natriuretic peptides).
  • Assessment of nonclinical factors (e.g., work stress, social isolation) in risk prediction.

Main Results:

  • Established clinical models have limitations in identifying all at-risk individuals.
  • Novel biomarkers can enhance prediction, but cost and equity concerns exist.
  • Nonclinical factors show association with CVD risk and potential for baseline stratification.

Conclusions:

  • A stepwise approach using nonclinical methods followed by clinical risk scores is proposed for initial screening.
  • Novel biomarkers may be reserved for enhanced stratification in a cost-effective manner.
  • Integrating nonclinical factors could improve population-level cardiovascular risk assessment and reduce health inequalities.