Abstract P070: Long-term Cumulative Blood Pressure Improves CVD Risk Prediction Algorithms

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Background: Published risk prediction algorithms only include current BP; however, long-term BP patterns are associated with atherosclerotic (AS)CVD incidence. We tested whether the long-term (5- and 10-year) cumulative blood pressure improves 10 year ASCVD prediction.

Methods: This study used the Lifetime Risk Pooling Project (LRPP) including the Framingham, CARDIA and ARIC cohorts. Participants with 15- and 20-year follow-up (5- and 10- years prior to risk calculation and 10 year follow-up), no history of prior CVD, and between the ages of 45 and 65 at the time of risk estimation were included. We calculated 10 year ASCVD risk using the 2013 ACC/AHA 10-year ASCVD Pooled Cohort Equations. Study-specific coefficients were calculated. Differences in the C-statistic, the category-free net reclassification index (NRI) and improved discrimination index (IDI) were examined between the model with baseline as compared to the model with cumulative SBP. Analyses were stratified by gender.

Results: Among 11,475 individuals (42.4% male and 12.7% African American), those in the highest tertile of cumulative SBP were older, more likely to be male, and had a higher burden of other CVD risk factors. Overall, 1,487 (13%) participants experienced a CVD event (mean follow-up time was 12 years). ASCVD incidence rates increased with higher tertiles of cumulative SBP from 4 events per 1,000 person-years in the lowest tertile to 8 and 18 in the second and third tertiles, respectively. No significant improvements were seen in the C-statistic when including 5- or 10-year cumulative SBP (see table). However, the replacement cumulative SBP resulted in significant improvements in model reclassification of NRI and IDI with greater improvements for the 10- than 5-year cumulative measure.

Conclusions: Measures of cumulative BP can improve the ability of CVD risk prediction models to correctly classify individuals. Additional studies on the inclusion of these measures in future risk prediction algorithms are warranted.

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