Individual versus integration of multiple components of central blood pressure and aortic stiffness in predicting cardiovascular mortality in end-stage renal diseases.
Nadège CôtéCatherine FortierLouis-Charles DesbiensJános NemcsikMohsen AgharaziiPublished in: Journal of human hypertension (2024)
Aortic stiffness, measured by carotid-femoral pulse wave velocity (PWV), is a predictor of cardiovascular (CV) mortality in patients with end-stage renal disease (ESRD). Aortic stiffness increases aortic systolic and pulse pressures (cSBP, cPP) and augmentation index adjusted for a heart rate of 75 beats per minute (AIx@75). In this study, we examined if the integration of multiple components of central blood pressure and aortic stiffness (ICPS) into risk score categories could improve CV mortality prediction in ESRD. In a prospective cohort of 311 patients with ESRD on dialysis who underwent vascular assessment at baseline, 118 CV deaths occurred after a median follow-up of 3.1 years. The relationship between hemodynamic parameters and CV mortality was analyzed through Kaplan-Meier and Cox survival analysis. ICPS risk score from 0 to 5 points were calculated from points given to tertiles, and were regrouped into three risk categories (Average, High, Very-High). A strong association was found between the ICPS risk categories and CV mortality (High risk HR = 2.20, 95% CI: 1.05-4.62, P = 0.036); Very-High risk (HR = 4.44, 95% CI: 2.21-8.92, P < 0.001) as compared to the Average risk group. The Very-High risk category remained associated with CV mortality (HR = 3.55, 95% CI: 1.37-9.21, P = 0.009) after adjustment for traditional CV risk factors as compared to the Average risk group. While higher C-statistics value of ICPS categories (C: 0.627, 95% CI: 0.578-0.676, P = 0.001) was not statistically superior to PWV, cPP or AIx@75, the use of ICPS categories resulted in a continuous net reclassification index of 0.56 (95% CI: 0.07-0.99). In conclusion, integration of multiple components of central blood pressure and aortic stiffness may potentially be useful for better prediction of CV mortality in this cohort.
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