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Performance of the SCORE and Globorisk cardiovascular risk prediction models: a prospective cohort study in Dutch
Merle Ca Schoofs1, Reinier P Akkermans2, Wim Jc de Grauw1
1Department of Primary and Community Care, Radboud University Medical Center, Nijmegen, the Netherlands.
Insights
Cardiovascular disease (CVD) risk prediction models used by Dutch GPs show poor performance, underestimating 10-year risks. This impacts patient identification and management for high-risk individuals.
Area of Science:
- Cardiology
- Public Health
- General Practice Research
Background:
- General practitioners (GPs) routinely utilize 10-year cardiovascular disease (CVD) risk estimations to identify high-risk patients.
- Accurate CVD risk prediction is crucial for timely intervention and patient management in primary care settings.
Purpose of the Study:
- To evaluate the predictive performance of four distinct CVD risk models within Dutch general practice.
- To assess the discrimination and calibration of SCORE, SCORE-fatal and non-fatal (SCORE-FNF), Globorisk-laboratory, and Globorisk-office models.
Main Methods:
- A prospective cohort study utilizing routine data from 46 Dutch general practices (2009-2019).
- Data linkage with cause of death statistics to determine CVD outcomes.
- Performance assessment based on discrimination and calibration metrics for fatal and non-fatal CVD events.
Main Results:
- All evaluated models demonstrated poor discrimination and calibration.
- The SCORE-FNF model, currently used in Dutch general practice, significantly underestimated 10-year CVD risk across all predicted risk deciles.
- The SCORE model's performance could not be adequately assessed due to a limited number of fatal CVD events.
Conclusions:
- Current CVD risk prediction models exhibit serious underestimation of 10-year fatal and non-fatal CVD risk in Dutch general practice.
- Discrepancies between patient populations eligible for risk prediction and those used in model development may explain the poor performance.
- The restriction of the SCORE model to fatal CVD outcomes limits its utility in routine Dutch general practice.
Background:
GPs frequently use 10-year-risk estimations of cardiovascular disease (CVD) to identify high- risk patients.
Aim:
To assess the performance of four models for predicting the 10-year risk of CVD in Dutch general practice.
Design And Setting:
Prospective cohort study. Routine data (2009- 2019) was used from 46 Dutch general practices linked to cause of death statistics.
Method:
The outcome measures were fatal CVD for SCORE and first diagnosis of fatal or non- fatal CVD for SCORE fatal and non-fatal (SCORE- FNF), Globorisk-laboratory, and Globorisk-office. Model performance was assessed by examining discrimination and calibration.
Results:
The final number of patients for risk prediction was 1981 for SCORE and SCORE-FNF, 3588 for Globorisk-laboratory, and 4399 for Globorisk- office. The observed percentage of events was 18.6% (n = 353) for SCORE- FNF, 6.9% (n = 230) for Globorisk-laboratory, 7.9% (n = 323) for Globorisk-office, and 0.3% (n = 5) for SCORE. The models showed poor discrimination and calibration. The performance of SCORE could not be examined because of the limited number of fatal CVD events. SCORE-FNF, the model that is currently used for risk prediction of fatal plus non-fatal CVD in Dutch general practice, was found to underestimate the risk in all deciles of predicted risks.
Conclusion:
Wide eligibility criteria and a broad outcome measure contribute to the model applicability in daily practice. The restriction to fatal CVD outcomes of SCORE renders it less usable in routine Dutch general practice. The models seriously underestimate the 10-year risk of fatal plus non-fatal CVD in Dutch general practice. The poor model performance is possibly because of differences between patients that are eligible for risk prediction and the population that was used for model development. In addition, selection of higher-risk patients for CVD risk assessment by GPs may also contribute to the poor model performance.
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