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Interpretation of CVD risk predictions in clinical practice: Mission impossible?

G R Lagerweij1,2, K G M Moons1, G A de Wit1,3

  • 1Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, the Netherlands.

Plos One
|January 10, 2019
PubMed

Insights

Different cardiovascular disease (CVD) risk prediction models yield varied outcomes and risk estimates. This leads to inconsistent treatment recommendations, impacting preventive strategies for CVD.

Area of Science:

  • Cardiology
  • Public Health
  • Epidemiology

Background:

  • Cardiovascular disease (CVD) risk prediction models are crucial for identifying high-risk individuals to reduce population-level CVD burden.
  • Discrepancies in predicted CVD outcomes among models can lead to variations in identifying high-risk individuals.

Purpose of the Study:

  • To investigate whether different CVD risk prediction models lead to varying treatment recommendations in clinical practice.
  • To compare the impact of four widely used CVD risk prediction models on treatment decisions.

Main Methods:

  • Defined predicted outcomes for ATP-III, Framingham (FRS), Pooled Cohort Equations (PCE), and SCORE models using ICD-10 codes.
  • Applied these models to a Dutch population cohort (n=18,137) to estimate 10-year CVD risks.
  • Compared treatment recommendations based on model-specific risks and thresholds.

Main Results:

  • Predicted 10-year CVD risks varied significantly: 1.2% (ATP), 5.2% (FRS), 1.9% (PCE), and 0.7% (SCORE).
  • Preventive drug prescriptions varied widely: 0.2% (ATP), 14.9% (FRS), 4.4% (PCE), and 2.0% (SCORE).

Conclusions:

  • Widely used CVD prediction models differ substantially in outcomes and risk estimates, precluding direct comparison.
  • Model choice and its associated risk threshold significantly influence treatment decisions, causing practice variation in CVD prevention.
Abstract

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