We face an "epidemic" of hospitalizations in heart failure (HF), which are often unrelated to HF. We hypothesize that extensive efforts have not successfully reduced hospitalizations in HF because management standards rely on disease-centric clinical guidelines, applied within provider-centric systems of care. Care models often overlook that the HF syndrome arises within a complex and poorly understood multi-morbidity context and how comorbid conditions present according to the type of HF (preserved vs. reduced EF) is not known. Further, as a chronic disease that coexists with multiple other conditions, HF causes self-management difficulties in elderly patients who may lack the needed support. Understanding the epidemiology of coexisting conditions and the determinants of successful self-management is fundamental to design new practice models and reduce hospitalizations in HF. The central goal of our revised application is to respond to the urgent need for new approaches by fulfilling 3 objectives: firstly, understand the complex epidemiology of coexisting conditions in HF, secondly, identify patient-centric attributes conducive to successful self-management by drawing upon the Chronic Care Model, thirdly, develop models that integrate cardiovascular characteristics, coexisting conditions and determinants of self-care to be used at the point of care to engage community support for at risk patients. Our 3 Specific Aims, designed to address these objectives, align with the priorities of the Department of Health and Human Services, outlined in their report "Multiple Chronic Conditions: A Strategic Framework".
Aim 1 will study the epidemiology of coexisting conditions in HF in a community cohort of persons with HF within the Rochester Epidemiology Project. We will assess the prevalence of coexisting conditions (comorbid diseases, geriatric syndromes) and determine which clusters of conditions most impact hospitalizations. We will assess the emergence and progression of coexisting conditions after HF diagnosis and their association with hospitalizations, according to the type of HF (preserved vs. reduced EF).
Aim 2 will evaluate the role of patient-centric factors (social support and self-management) on hospitalizations in HF guided by the Chronic Care Model in a prospective cohort of patients living with HF.
Aim 3 will develop and evaluate prediction models to identify patients with HF at high risk for hospitalizations. In doing so, we will leverage the unique Health Information Exchange (HIE) capabilities and community partnerships established by the Office of the National Coordinator for Health Information Technology-Funded Southeast Minnesota Beacon Program and assess the feasibility of adapting an existing HIE architecture to implement such models at the point of care, identify patients at high risk for hospitalizations and alert community care coordinators when their patients have been hospitalized to enable proactive community support. By executing these aims, we will understand how the epidemiology of coexisting conditions and how patient-centric factors contribute to the burden of hospitalizations in HF. We will develop risk prediction models, which integrate cardiovascular characteristics, coexisting conditions and patient-centric factors to optimize tools for interventions to reduce hospitalizations in HF.
This research will provide important information on how coexisting conditions and patient-centric factors (social support and patient self-management) contribute to the burden of hospitalizations in heart failure. It will determine key predictors of hospitalizations and help optimize tools for point of care interventions to reduce hospitalizations