Independent external validation and head-to-head comparison of guideline-recommended CVD risk prediction models

Lum Kastrati1, Eleftheria Maria Alexandri2, Maurice Rupp1

  • 1Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.

Insights

Three major cardiovascular disease (CVD) prediction models showed similar performance in UK Biobank data, despite variations in their development. Further research needs standardized definitions for predictors and outcomes in CVD risk assessment.

Area of Science:

  • Cardiology
  • Epidemiology
  • Biostatistics

Background:

  • Cardiovascular disease (CVD) prediction models vary in populations, predictors, and outcome definitions, leading to unclear implications for risk estimation.
  • Direct comparisons of these guideline-endorsed models are limited, hindering understanding of their real-world applicability.

Purpose of the Study:

  • To conduct a head-to-head comparison of the performance and transportability of three guideline-endorsed CVD risk prediction models: PREVENT, SCORE2, and QRISK3.
  • To specifically evaluate the sex-specific performance and accuracy of these models.

Main Methods:

  • External validation of PREVENT, SCORE2, and QRISK3 models using UK Biobank (UKBB) data from 502,157 participants.
  • Assessment of model performance using Brier scores, Area Under the Receiver Operating Characteristic Curve (AUC), and calibration, stratified by sex.
  • Risk of bias evaluated using the PROBAST tool.

Main Results:

  • Models showed substantial variation in predictors, populations, and outcome definitions.
  • Overall predictive performance was generally better in females (AUC 0.7092-0.7468) than males (AUC 0.6813-0.6946).
  • Calibration was suboptimal, especially in older individuals, with systematic risk overestimation; all models had a high risk of bias.

Conclusions:

  • Despite heterogeneity, PREVENT, SCORE2, and QRISK3 demonstrated similar performance in the UKBB cohort.
  • Future research should prioritize prospective studies with standardized definitions for predictors and outcomes in CVD risk assessment.
Abstract

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