RETHINK MEDICAL, INC. — Department of Health and Human Services SBIR Phase II: NHLBI

RETHINK MEDICAL, INC. — SBIR Phase II award from Department of Health and Human Services.

Amount
$1,499,469
Agency
Department of Health and Human Services · National Institutes of Health
Program / Phase
SBIR · Phase II
Topic
NHLBI
Solicitation
PA15-269
NAICS
Place of performance
CA
Period
2016-08-22 → 2019-07-31

Description

ReThink Medical Inc proposes to develop an ambulatory version of the physiologic monitoring technology it validated in Phase I the CorBand The CorBand is designed to track key physiologic parameters that are predictive of cardiac decompensation to detect the early stages of decompensation and to relay both the physiologic data and generated alerts to relevant caregivers with the end goal of reducing hospitalizations by enabling proactive preventative care in the heart failure population Heart failure effects million individuals in the US and the costs associated are approaching $ billion annually This technology will not only improve the quality of life for heart failure patients but also help to reduce this massive burden on the US healthcare system Our long term objective is to help drive the transformation from the reactive pay for service healthcare paradigm to one that puts emphasis on preventative care through health monitoring technologies Our specific aims include manufacturing a robust version of the device for use in two large long term ambulatory studies It will be validated using patients who have cardiac implantable electronic devices CIEDs Heart rate HR heart rate variability HRV activity levels and fluid index derived from the CorBand will be compared with data collected from CIEDs manufactured by Medtronic and Boston Scientific and analyzed for correlation The CorBand will be deployed in a large ambulatory population of HF patients We will collect the patientandapos s clinical notes and note hospitalization events along with remote physiologic data from the CorBand We will use this data to further build a predictive model for cardiac decompensation using a number of machine learning approaches In aim we will perform a controlled study on a population of HF patients All the subjects will wear the CorBand we will use the data from half the subjects to predict decompensation events provide that information to subjectandapos s caregiver in order to provide preventative care The other half will receive usual care We will then examine the number of HF related hospitalizations in both groups to evaluate the ability of the CorBand to reduce hospitalizations Our goal is to simplify the management of heart failure while improving the quality of care of the patients and reducing the associated costs We will investigate the benefits of a novel non invasive multi parameter sensor with telemetry for monitoring patients with heart failure combined with an algorithm for detecting early stage cardiac decompensation with a goal of preventing hospitalizations