Background Biomarker based preventative and monitoring strategies are increasingly used for risk stratification in cardiovascular (CV) disease. Natriuretic peptides and soluble (s)ST2 can identify patients at-risk of CV events. However, most of the existing reports focus on single time point analysis and in populations who have already exhibited major adverse CV events (MACE).
Objective The objective of this study was to investigate the utility of longitudinal change in B-type natriuretic peptide (BNP) and sST2 concentrations for predicting incident MACE (heart failure, myocardial infarction, arrhythmia, stroke/transient ischaemic attack and CV death) in asymptomatic community-based patients with risk factors but without prevalent MACE at enrolment.
Study design, size, and duration The study population consisted of 282 patients selected from within the longitudinal STOP-HF study of asymptomatic patients with risk factors for development of MACE. Fifty of these patients developed a MACE. The study was run in two phases comprising of an initial investigative cohort (n = 195) and a subsequent 2:1 (No MACE:MACE) propensity matched verification cohort (n = 87). BNP and sST2 were quantified in all patients at two time points a median of 2.5 years apart.
Results Fifty two subjects developed incident MACE. Longitudinal change in sST2 was a statistically significant predictor of incident MACE, with an area under the curve (AUC) for sST2 longitudinal change to predict incident MACE of 0.60 (95% confidence interval 0.52–0.68). A one-unit increment in sST2 change from baseline to follow up corresponded to approximately 7.99% increase in the rate of one or more incident MACE via Cox modelling, independent of the baseline or follow-up concentration. In contrast, longitudinal change value of BNP was not associated with MACE.
Conclusion Longitudinal change in sST2 but not BNP was associated with incident MACE in asymptomatic, initially event free patients in the community. Further work is required to evaluate the clinical utility of change in sST2 in risk prediction and event monitoring in this setting.
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