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Summary

Comparing cardiovascular disease risk models shows newer tools like SCORE2 may better identify high-risk patients for statin therapy. However, older models like the Framingham Risk Score prevent more events overall.

Keywords:
Framingham Risk ScorePooled Cohort EquationsPrimary preventionRisk modelsSCORE2Statins

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Area of Science:

  • Cardiology
  • Preventive Medicine
  • Health Outcomes Research

Background:

  • Guidelines lack consensus on optimal cardiovascular disease (CVD) risk models for primary prevention.
  • Risk stratification is crucial for targeted interventions like statin therapy.

Purpose of the Study:

  • To compare the effectiveness of different CVD risk models in primary prevention.
  • To evaluate potential improvements in Number Needed to Treat (NNT) and Number of Events Prevented (NEP) across models.

Main Methods:

  • Retrospective analysis of 47,399 primary care patients (aged 40-75) in Ontario, Canada (2010-2014).
  • Risk estimation using Framingham Risk Score (FRS), Pooled Cohort Equations (PCEs), recalibrated FRS (R-FRS), SCORE2, and low-risk region recalibrated SCORE2 (LR-SCORE2).
  • Follow-up for up to 5 years to assess CVD events.

Main Results:

  • SCORE2 demonstrated the lowest NNT (40) for statin therapy, identifying higher-risk patients more efficiently.
  • The Framingham Risk Score (FRS) had the highest NNT (65) but resulted in the highest Number of Events Prevented (NEP) (406).
  • Newer models like SCORE2 recommended statins to a smaller proportion of patients (7.9%) compared to FRS (34.6%).

Conclusions:

  • Newer risk models, such as SCORE2, may enhance the allocation of statins to higher-risk individuals, indicated by a lower NNT.
  • Despite improved targeting, these newer models might prevent fewer overall cardiovascular events at a population level compared to traditional models.