Predictive performance of cardiovascular disease risk prediction models in older adults: a validation and updating study
Authors' conclusion
Updating models, by adjusting model coefficients to better reflect the characteristics and risk factors of older adults, improves CVD risk prediction in a large cohort of relatively healthy Caucasian population aged 70+.
Study context
- Population
- Australian and US community-dwelling older adults without history of cardiovascular disease (CVD) events, dementia or physical disabilitymean age 75 (4.4) years
- Participants
- 15,618 participants
- Comparison
- Original, recalibrated and updated CVD risk prediction models
- Outcome
- Predicted CVD risk from adjudicated CVD events:
- Discrimination by C statistic
- Calibration
- Clinical utility by decision curves
- Follow-up
- Median 6.3 (IQR: 5.2-7.7) years
Methodological detailscohort from the ASPREE trial and ASPREE-XT observational post-trial follow-up
- Cohort from the ASPREE trial and ASPREE-XT observational post-trial follow-up
Performance
As reported in the abstract
Discrimination of updated models (C statistic)
C statistics of updated models, men
0.62-0.64
C statistics of updated models, women
0.68-0.69
Added net benefit at risk thresholds, women
4%-10%
Added net benefit at risk thresholds, men
5%-12%
Further findings
CVD events · median follow-up 6.3 (IQR: 5.2-7.7) years
- Men with CVD events
- 520
- Women with CVD events
- 498
Extended models (IRSAD, depression, serum creatinine)
- Did not improve CVD risk discrimination of updated models
Original publication
Predictive performance of cardiovascular disease risk prediction models in older adults: a validation and updating study. Heart. Published 14 May 2025. doi:10.
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