Transfer function-derived central pressure and cardiovascular disease events: the Framingham Heart Study

Abstract

Background: Relations between central pulse pressure (PP) or pressure amplification and major cardiovascular disease (CVD) events are controversial. Estimates of central aortic pressure derived using radial artery tonometry and a generalized transfer function may better predict CVD risk beyond the predictive value of brachial SBP.

Methods: Augmentation index, central SBP, central PP, and central-to-peripheral PP amplification were evaluated using radial artery tonometry and a generalized transfer function as implemented in the SphygmoCor device (AtCor Medical, Itasca, Illinois, USA). We used proportional hazards models to examine relations between central hemodynamics and first-onset major CVD events in 2183 participants (mean age 62 years, 58% women) in the Framingham Heart Study.

Results: During median follow-up of 7.8 (limits 0.2-8.9) years, 149 participants (6.8%) had an incident event. Augmentation index (P = 0.6), central aortic systolic pressure (P = 0.20), central aortic PP (P = 0.24), and PP amplification (P = 0.15) were not related to CVD events in multivariable models that adjusted for age, sex, brachial cuff systolic pressure, use of antihypertensive therapy, total and high-density lipoprotein cholesterol concentrations, smoking, and presence of diabetes. In a model that included standard risk factors, model fit was improved (P = 0.03) when brachial systolic pressure was added after central, whereas model fit was not improved (P = 0.30) when central systolic pressure was added after brachial.

Conclusion: After considering standard risk factors, including brachial cuff SBP, augmentation index, central PP and PP amplification derived using radial artery tonometry, and a generalized transfer function were not predictive of CVD risk.

FIGURE 1

Power analysis for models that include covariates that are correlated with a primary predictor variable. Panel A illustrates the minimum hazard ratio required to achieve 80% power to detect an effect with 149 events when covariates in the model explain a proportion of variance (R²) in the primary predictor. Panel B illustrates the number of events required to achieve 80% power for a fixed hazard ratio of 1.30.