Multi-Biomarker Profiling and Recurrent Hospitalizations in Heart Failure

Background: Despite advances in pharmacologic therapy and devices, patients with heart failure (HF) continue to have significant rehospitalization rates and risk prediction remains challenging. We sought to explore the value of a multi-biomarker panel (including NT-proBNP, hs-TnT, and ST2) on top of clinical assessment for long-term prediction of recurrent hospitalizations in HF. Methods and Results: NT-proBNP, hs-TnT, and ST2 levels were measured in 891 consecutive ambulatory HF patients. The independent association between the multi-biomarker panel and recurrent hospitalizations was assessed through a multivariable negative binomial regression and expressed as incidence rates ratios. McFadden pseudoR2 and goodness-of-fit measures were also used. The total number of unplanned hospitalizations (all-cause, cardiovascular [CV]-, and HF-related) were selected as the primary endpoints. At a mean follow-up of 4.2±2.1 years, 1623 all-cause hospitalizations in 498 patients (55.9%), 710 CV-related hospitalizations in 331 patients (37.2%), and 444 HF-related hospitalizations in 214 patients (24.1%) were registered. The crude incidence of all-cause, CV-, and HF-related recurrent hospitalizations was significantly higher for patients with the multi-biomarker panel above the cut-point (hs-TnT>14 ng/L, NT-proBNP>1000 ng/L, and ST2>35 ng/mL) (all P<0.001). For all-cause, CV-, and HF-related recurrent hospitalizations, the McFadden R2, Akaike information criterion, and Bayesian information criterion supported the superiority of incorporating the multi-biomarker panel into a clinical predictive model. Conclusions: A multi-biomarker approach that incorporates NT-proBNP, hs-TnT, and ST2 better identifies HF patients at risk for recurrent hospitalizations. Elucidation of new biophysiological targets for recurrent hospitalizations may identify patient profiles for focused intervention.