Background Heart failure (HF) represents a major health burden, with 80% of the HF health care costs attributable to hospitalizations. Reducing HF readmissions is a major VA strategic goal. Our pilot study demonstrated that multivariate physiological telemetry using a small wearable sensor has a high compliance rate and provides accurate early detection of impending readmission for HF exacerbation. We now propose to implement non-invasive remote monitoring at 5 VA medical centers. Significance/Impact Despite treatment advances, hospitalizations for HF exacerbation remain prevalent and costly. Accurate and timely detection of incipient HF exacerbation may be one path to reducing HF readmissions. Innovation The study will develop an implementation strategy for noninvasive remote monitoring with predictive analytics and an algorithmic treatment response to clinical alerts coming from the analytical platform. The result will be a reliable link between the clinical alert and an intervention that can affect the clinical outcome of the patient. Algorithmic response to the device alert will be a subject of ongoing validation and update as part of the learning health-care system concept. This will allow for integration with the electronic health record, optimization and standardization of the response process and decrease alert fatigue. Furthermore, we will evaluate patient and provider attitudes toward using remote monitoring to guide HF therapy, as well as the impact of this approach on key clinical outcomes. Specific Aims Aim 1. Implement remote monitoring into the clinical workflow of HF care. Aim 1a. Design implementation strategies for non-invasive remote monitoring and algorithmic response to clinical alerts generated by the predictive analytics platform. Aim 1b. Evaluate implementation outcomes, including clinician and patient perceptions and adoption of the use of ambulatory remote monitoring data. Aim 2. Conduct a feasibility study of non-invasive remote monitoring in chronic HF. Aim 2a. Define key characteristics that will inform design of a pivotal trial of non-invasive remote monitoring aimed at reducing rehospitalization and improving quality of life in HF. Aim 2b. Identify costs associated with implementation and non-invasive remote monitoring in HF. Methodology We will design implementation processes using the i-PARiHS framework and three implementation phases: 1) implementation intervention planning; 2) formative evaluation of pilot implementation at 2 vanguard sites; and 3) Implementation fidelity monitoring. We will enroll 240 patients hospitalized for HF exacerbation at 5 participating VA centers. All study subjects will receive the monitoring kit, which will be used for 90 days after discharge. Subjects will be randomized 1:1 to an intervention arm, where clinicians will be notified of clinical alerts and will follow response algorithm to modify HF treatment or recommend urgent clinic visit/emergency room visit, and to the control arm, where information from the sensors will be collected, but clinical alerts will not be generated or communicated to providers. Study outcomes will include the proportion of randomized patients who meet the algorithm’s criteria for at least one alert, the proportion of time the remote monitor is in use and functioning properly, HF hospitalization rate, hospital stay length, and health-related quality of life. Next Steps This work will inform design of a pivotal trial of non-invasive remote monitoring aimed at reducing rehospitalization and improving quality of life in HF. Implementation strategies developed in this study may be used not only for implementation of the monitoring approach tested, but also for other remote monitoring strategies in HF and additional chronic health conditions.
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Grant Number: I01HX002922-01A2
None at this time.
Treatment - Implementation, TRL - Applied/Translational, Treatment - Observational
Cardiovascular Disease, Data Management, Surveillance, Symptom Management
None at this time.