Related Experiment Video
Updated: Jul 4, 2026

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
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.
Background:
Cardiovascular disease (CVD) prediction models recommended by guidelines are developed using different populations, predictors, and outcome definitions. The implications of this heterogeneity for risk estimation are unclear, and direct comparisons remain limited.
Objectives:
Head-to-head comparison of the performance and transportability of three guideline-endorsed CVD risk prediction models, focusing on their sex-specific performance.
Methods:
We evaluated models recommended by the American Heart Association (PREVENT), European Society of Cardiology (SCORE2), and the National Institute for Health and Care Excellence (QRISK3). Risk of bias was assessed using the PROBAST tool. External validation was performed using the UK Biobank (UKBB) in a primary analysis including all participants with complete data for all models, enabling direct comparison, and in a secondary analysis applying each model to participants meeting its original eligibility criteria. Model performance was assessed using Brier scores, Area Under the Receiver Operating Characteristic Curve (AUC), and calibration across original and alternative outcome definitions, stratified by sex.
Results:
The PREVENT, SCORE2, and QRISK3 models varied substantially in terms of predictors, populations, and outcome definitions. We used data from 502,157 UKBB participants for the external validation in the primary analysis. Overall predictive performance (discrimination & calibration), as measured by Brier scores, was generally better in females. The AUC (95% CI) ranged from 0.7092 (0.7090-0.7094) to 0.7468 (0.7465-0.7471) for female and 0.6813 (0.6812-0.6814) to 0.6946 (0.6945-0.6946) for male populations. Calibration was suboptimal, particularly for older individuals, with systematic overestimation of risk. The models showed consistent performance when applied to different outcomes. All models were at high risk of bias.
Conclusion:
Despite heterogeneity in populations, predictors, and outcome definitions, PREVENT, SCORE2, and QRISK3 showed similar performance in the UKBB. Future studies should focus on prospective and standardized definitions and assessment of candidate predictors and outcomes.
Related Concept Videos
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Pre-Procedural Guidelines for Assessing Blood Pressure
Pharmacokinetic Models: Comparison and Selection Criterion
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Relative Risk
Sensitivity, Specificity, and Predicted Value
Sensitivity is the...