Home monitoring for heart failure management

Abstract

With a prevalence of 5.8 million in the United States alone, heart failure (HF) is a common syndrome associated with substantial morbidity, mortality, and healthcare expenditures. Close to 1 million HF hospitalizations occur annually in the United States, with the majority of these resulting from worsening congestion in patients previously diagnosed with HF. An estimated $37.2 billion is spent each year on HF in the United States. These statistics emphasize the need to develop and implement more effective strategies to assess, monitor, and treat HF. It has also become increasingly apparent that interventions geared toward identifying and monitoring subclinical congestion would be of value in the home management of chronic HF. Earlier identification and treatment of congestion together with improved care coordination, management of comorbid conditions, and enhanced patient self-management may help to prevent hospitalizations in patients with chronic HF. Such home monitoring extends from the promotion of self-care and home visitations to telemedicine and remote monitoring of external or implantable devices. This paper discusses the challenges in monitoring patients with HF, reviews clinical trials testing different monitoring strategies in HF, and highlights ongoing investigations into the optimal approaches to home monitoring for HF.

Figure 1. Usual care versus hemodynamic monitored and guided home care for patients with heart failure

Comparison of monitoring, assessments, visits, and the approach to therapy with usual care compared to hemodynamic monitored and guided home care for heart failure.

Figure 2. Home hemodynamic monitoring of chronic heart failure

Changes in therapy can potentially be made more rapidly in response to wirelessly collected hemodynamic data. Even before symptoms develop, data is collected and alerts can be sent to a clinician for review, or personalized treatment recommendations can be automatically generated based on a prespecified algorithm.