There are over five million Americans living with heart failure (HF), and another 670,000 new cases being diagnosed each year. HF is a characteristically unstable condition that is the most costly diagnosis in the Medicare population and is the most common cause for hospitalization. The instability of HF disease is reflected in short-term fluctuations of the HF disease biomarker, B-Type Natriuretic Peptide (BNP). Patient self-management behaviors are important for minimizing HF disease instability. Depression is often comorbid with HF, and elevated depressive symptoms are associated with a marked increase in adverse clinical outcomes. For both depressed and non-depressed HF patients, worsening depressive symptoms mark a substantially increased risk of cardiovascular hospitalization or death. Despite the risk associated with depressive symptoms, the nature of their association with a worsening HF disease trajectory and adverse clinical outcomes is poorly understood. Converging evidence suggests that the association between depressive symptoms and accelerated HF disease progression may involve multiple behavioral and pathophysiological pathways. This application proposes an innovative, prospective bio-behavioral monitoring study of 220 HF patients with systolic dysfunction that is designed to address the issue of how depressive symptoms and their bio-behavioral manifestations are implicated in worsening HF disease. Using newly developed home-monitoring biotechnology, we propose to track fluctuations in HF disease severity using weekly assessments of BNP over a 16-week period. Symptoms of depression and HF-related health behaviors also will be assessed weekly via concurrent monitoring. This weekly bio-behavioral monitoring will be framed by comprehensive baseline and 4-month assessments of depression, HF disease severity, and pathophysiological mechanisms that have been related to the presence of depressive symptoms and implicated in the progression of HF disease. Clinical outcomes also will be assessed over a subsequent 2- year follow-up period. The proposed study will create a unique data structure that will allow us to use contemporary statistical methods that will serve to elucidate causal associations between depressive symptoms, self-management health behaviors, pathophysiological processes, and HF disease progression and clinical outcomes. The study findings are expected to yield important advances in our understanding of why depressive symptoms may be particularly detrimental in the presence of HF and will help to inform the design of future clinical trials.
With over five million Americans living with heart failure, and another 670,000 new cases diagnosed each year, HF is the most costly diagnosis in the Medicare population. Clinical depression is strikingly common in heart failure patients, and not only diminishes their quality of life, but also is associated with a markedly increased risk of hospitalization or death. The study proposed in this application is designed to further our understanding of the behavioral and biological effects of depression in patients with heart failure, so that appropriate treatments can be developed.