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Agreement Between Cardiovascular Risk Assessment Models (SCORE2, Framingham, ASCVD-2013, and SCORE2-OP) in Adults
Cristian Orlando Porras Bueno1,2, Hector Henry Lindarte Vargas1,2, Maria Paula Ravagli Baquero1,2
1Departamento de Medicina Interna, Pontificia Universidad Javeriana, Bogotá, Colombia.
Cardiovascular risk (CVR) models need regional validation in adults over 70. The SCORE2-OP model may overestimate CVR compared to locally validated scores, emphasizing the need for accurate CVR estimation in older populations.
Area of Science:
- Cardiology
- Geriatrics
- Epidemiology
Background:
- Existing cardiovascular risk (CVR) prediction models are often developed for younger populations.
- The SCORE2-OP model was created to improve CVR estimation in adults aged 70 years and older.
- The applicability of SCORE2-OP outside Europe requires investigation due to potential over- or underestimation of CVR.
Purpose of the Study:
- To evaluate the agreement between four common CVR prediction tools and the SCORE2-OP model in older adults (≥70 years).
- To assess the concordance of Framingham, ASCVD-2013, SCORE2, and SCORE2-OP in a Colombian cohort.
Main Methods:
- Secondary analysis of the SABE Colombia study (2015) involving adults aged 70 years and older.
- Concordance assessed using Cohen's quadratically weighted kappa and Lin's concordance correlation coefficients.
- Four CVR tools (Framingham, ASCVD-2013, SCORE2, SCORE2-OP) were compared.
Main Results:
- Analysis included 586 participants aged ≥70 years.
- Fair agreement was found between SCORE2-OP and Colombian-calibrated ASCVD-2013 (kappa=0.24) and Framingham (kappa=0.22) models.
- Moderate agreement was observed between SCORE2-OP and the Colombian-calibrated SCORE2 model (kappa=0.43).
Conclusions:
- There is variable agreement between established CVR prediction models and SCORE2-OP in Colombian adults aged 70 years and older.
- The SCORE2-OP model tended to overestimate cardiovascular risk compared to nationally validated scores.
- Region-specific validation of CVR prediction models is crucial for accurate risk assessment in elderly populations.
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