Does SBP Burden better predict major adverse cardiovascular events compared to traditional SBP indices in high-risk, non-diabetic patients?
SBP Burden is a novel metric that outperforms traditional SBP indices in predicting cardiovascular outcomes in high-risk, non-diabetic patients.
Background: Conventional systolic blood pressure (SBP) indices, such as mean SBP or variability indices, fail to capture control consistency. SBP Time in Target Range (TTR) improves risk prediction by measuring SBP control duration. However, it neglects the magnitude of SBP elevation, treating minor and severe elevations equally. We developed and validated the SBP Burden, a novel metric integrating both the duration and magnitude of SBP elevation, to enhance cardiovascular risk prediction. Methods: This post-hoc analysis of the SPRINT included 9,017 high-risk, non-diabetic participants (age, 67.0 61.0 to 76.0 years; 64.7% men). SBP Burden was calculated as the proportion of the over-target time multiplied by the over-target part proportion of SBP area under the curve AUC during that time, using SBP records in months 0-6 (target: 130mmHg). Its prediction performance was compared with Mean SBP, SBP Standard Deviation (SD), SBP Average Real Variability (ARV), SBP TTR, and SBP AUC. The primary outcome was the first occurrence of major adverse cardiovascular events (MACEs), including cardiovascular death, myocardial infarction, stroke, and heart failure hospitalization. Results: Over a median follow-up of 3.89 years, 568 MACEs occurred. After adjusting for traditional risk co-variables, the SBP Burden showed an independent linear association with MACEs (Hazard Ratio HR, 95% confidence interval CI:1.17, 1.09-1.26; P<0.01), and remained even after further adjustment for SBP SD and ARV. Mean SBP (HR, 95%CI:1.13, 1.03-1.23; P<0.01), SBP SD (HR, 95%CI:1.10, 1.01-1.19; P=0.03), SBP ARV (HR, 95%CI:1.10, 1.01-1.18; P=0.02), SBP AUC (HR, 95%CI: 1.15, 1.05-1.26; P<0.01), and SBP TTR (HR,95%CI: 0.88, 0.80-0.97; P=0.01) were also independent risk predictors. Further, we confirmed SBP Burden gained the highest prediction improvement level in discrimination and reclassification (Net Reclassification Improvement as 0.12 0.03-0.22; Integrated Discrimination Improvement as 0.0032 0.0006–0.0076), and feature importance (relative informativeness and LASSO ranking as Top1) among tested SBP indices. Conclusion: In high-risk, non-diabetic patients, SBP Burden is an independent predictor of cardiovascular outcomes. It overcomes the limitation of SBP TTR, outperforming other SBP indices in predictive performance and feature importance for cardiovascular outcomes.
Li et al. (Thu,) studied this question.