BACKGROUND: The utility of single versus combined blood pressure (BP) components in predicting cardiovascular disease (CVD) events is not established. We compared systolic BP (SBP) and diastolic BP (DBP) versus pulse pressure (PP) and mean arterial pressure (MAP) combined and each of these 4 BP components alone in predicting CVD events. METHODS AND RESULTS: In participants in the original (n=4760) and offspring (n=4897) Framingham Heart Study who were free of CVD events and BP-lowering therapy, 1439 CVD events occurred over serial 4-year intervals from 1952 to 2001. In pooled logistic regression with the use of BP categories, combining SBP with DBP and PP with MAP improved model fit compared with individual BP components (P<0.05 to P<0.0001). Significant interactions were noted between SBP and DBP (P=0.02) and between PP and MAP (P=0.01) in their respective multivariable models. Models with continuous variables for SBP+DBP and PP+MAP proved identical in predicting CVD events (Akaike Information Criteria=10 625 for both). Addition of a quadratic DBP(2) term to DBP and SBP further improved fit (P=0.0016). CONCLUSIONS: Combining PP with MAP and SBP with DBP produced models that were superior to single BP components for predicting CVD, and the extent of CVD risk varied with the level of each BP component. The combination of PP+MAP (unlike SBP+DBP) has a monotonic relation with risk and may provide greater insight into hemodynamics of altered arterial stiffness versus impaired peripheral resistance but is not superior to SBP+DBP in predicting CVD events.
BACKGROUND: The utility of single versus combined blood pressure (BP) components in predicting cardiovascular disease (CVD) events is not established. We compared systolic BP (SBP) and diastolic BP (DBP) versus pulse pressure (PP) and mean arterial pressure (MAP) combined and each of these 4 BP components alone in predicting CVD events. METHODS AND RESULTS: In participants in the original (n=4760) and offspring (n=4897) Framingham Heart Study who were free of CVD events and BP-lowering therapy, 1439 CVD events occurred over serial 4-year intervals from 1952 to 2001. In pooled logistic regression with the use of BP categories, combining SBP with DBP and PP with MAP improved model fit compared with individual BP components (P<0.05 to P<0.0001). Significant interactions were noted between SBP and DBP (P=0.02) and between PP and MAP (P=0.01) in their respective multivariable models. Models with continuous variables for SBP+DBP and PP+MAP proved identical in predicting CVD events (Akaike Information Criteria=10 625 for both). Addition of a quadratic DBP(2) term to DBP and SBP further improved fit (P=0.0016). CONCLUSIONS: Combining PP with MAP and SBP with DBP produced models that were superior to single BP components for predicting CVD, and the extent of CVD risk varied with the level of each BP component. The combination of PP+MAP (unlike SBP+DBP) has a monotonic relation with risk and may provide greater insight into hemodynamics of altered arterial stiffness versus impaired peripheral resistance but is not superior to SBP+DBP in predicting CVD events.
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