Heart failure (HF) affects an estimated 6.2 million Americans over the age of 20 and
carries a very high healthcare system burden worldwide. Annual costs for HF management in
the United States were estimated at $30.7 billion in 2012 and are projected to increase
to $68.7 billion by 2030. The primary cost driver for HF management is a high rate of
acute decompensation and subsequent hospitalization. The mean per-patient cost of an
HF-related hospitalization is estimated to be $14,631.
Among the conditions that the Centers of Medicare and Medicaid Services (CMS) monitors
for their Hospital Readmission Reduction Program, HF has the highest median readmission
rate at days 1-29 (23%) and days 1-60 (11.4%) postdischarge. The cost burden of HF
readmission is $2.7 billion in 2013. A meta-analysis from 2012 estimated that 23.1% of HF
readmissions are avoidable, although individual studies ranged from 5% to 79%. Many
health plans, including CMS, have focused on interventions that monitor patients for
early detection of HF decompensation. Earlier interventions can help care teams prevent
avoidable hospitalizations.
Invasive hemodynamic sensor devices have enabled HF care teams to better predict and
prevent HF decompensation events, and thus prevent rehospitalizations. One such device is
the CardioMEMS pulmonary artery (PA) sensor (Abbott Inc., Atlanta, GA, USA). The
CardioMEMS is implanted in a branch of the left PA, allowing for daily measurements of PA
pressures. PA pressures are used as a surrogate marker of filling pressure, and rising
filling pressures, in turn, are a marker that precedes the exacerbation of HF. The
CHAMPIONS trial demonstrated that remote diuretic management using CardioMEMS reduced HF
all-cause hospitalizations by 43% and mortality by 57%. Unfortunately, CardioMEMS as an
HF solution is invasive, costly (average sales price of $17,750), indicated for a
restricted patient population (NYHA class III HF who have been hospitalized within the
last year), and has limited reimbursement coverage due to equivocal cost-effectiveness
projections.
This has stimulated a search for less expensive, non-invasive sensors that may correlate
with fluid status in HF patients. A study in Taiwan demonstrated that outpatient therapy
guided by an inpatient device with ECG and sound sensors reduced post-discharge HF
utilization by 31% when compared to a control group using symptoms to guide therapy. The
LINK-HF study demonstrated that a wearable patch with ECG and sound sensors could predict
HF readmissions with a sensitivity of 76% to 88%, a specificity of 85%, and a median lead
time of 6.5 days.
Despite these initially promising results, however, these devices have significant
disadvantages. The inpatient device used in the Taiwanese study could not be adapted into
a portable form factor for outpatient use. Wearable devices can be rigid, uncomfortable,
and highly visible, all of which can interfere with patient function and decrease
monitoring compliance.
Therefore, there remains an unmet clinical need for a widely available, non-invasive,
affordable medical device that can estimate an HF patient's hemodynamic fluid status and
inform the HF care management team. The ultimate goal remains to decrease an HF patient's
risk for hospital readmission, all from the comfort of the patient's home.
To meet this need, Eko has developed the DUO, an FDA-cleared, portable, hand-held,
wirelessly connected medical device with ECG and sound sensors. Data from the DUO can be
wirelessly streamed to a mobile phone or tablet, which can then be transmitted to a
HIPAA-compliant internet cloud infrastructure for storage and analysis. In 2020, Eko
introduced into the US market a package of AI/ML algorithms that follows this workflow to
identify heart murmurs, atrial fibrillation, and other cardiac conditions, and intends
after this proof of concept study to build upon this platform to estimate and trend PA
pressures.
But beyond measuring and trending PA pressures, the DUO can be used to capture additional
important HF features that will further improve any HF algorithm's performance. For
example, because patients with decompensated HF often have an audible third heart sound,
characteristic ECG findings, and altered time interval durations between their heart
sounds and ECG signals, the Eko DUO device may be uniquely positioned to detect these
types of changing signals.
In addition, because heart failure and fluid overload are reflected in the lungs as
crackles (and occasionally effusions), the lung examination is and has always been a
cornerstone of the overall physical examination of HF patients. By using the DUO to
capture lung sounds in patients with HF, and comparing not only the presence or absence
of crackles, but also how these adventitious sounds change over time, we will be able to
explore the utility of the Eko DUO in helping to predict exacerbated HF.
This proof-of-concept study evaluates the feasibility of the Eko DUO in capturing and
measuring signals relevant to HF exacerbation (e.g., time intervals, adventitious lungs
sounds, pathologic heart sounds), as well as the feasibility of developing an AI/ML
algorithm to model PA pressures in HF patients with the implantable CardioMEMS device.