TRUE-HF: Apple-CPET Ted Rogers Understanding Exacerbations of Heart Failure

Sponsor

University Health Network, Toronto (Other)

Overall Status

Recruiting

CT.gov ID

NCT05008692

Collaborator

Apple Inc. (Industry)

200

Enrollment

Location

14.9

Anticipated Duration (Months)

13.4

Patients Per Site Per Month

Study Details

Study Description

Brief Summary

Heart Failure (HF) is a complex disease associated with the highest burden of cost to the healthcare system. The cardiopulmonary exercise test (CPET) is instrumental in determining the prognosis of patients with HF. This study will evaluate whether aggregate biometric data from the Apple Watch combined with demographic, cardiac, and biomarker testing can improve our ability to predict heart failure outcomes among a diverse ambulatory HF population

Condition or Disease	Intervention/Treatment	Phase
Heart Failure Heart Failure, Systolic Heart Failure, Diastolic Heart Transplantation Heart Failure，Congestive

Detailed Description

Heart Failure (HF) has a prevalence of 3.5% suggesting that over one million Canadians are affected by this disease, and more than 50,000 are newly diagnosed each year. This complex disease is associated with the highest burden of cost to the healthcare system, attributable to hospitalizations, missed work, medications, and health care services

Traditionally, clinicians have relied on static snapshots of patients to determine clinical status and estimate prognosis. More advanced cardiac centers rely on cardiopulmonary exercise testing (CPET), where patients are further stratified based on validated exercise parameters. CPET remains underutilized and resource-intensive. It requires expensive equipment, proficient personnel, and clinicians with specialized training. Thus, there is an unmet need for a more widely available, accessible, and longitudinal assessment of clinical status to better monitor and prognosticate patients outside of the ambulatory setting. Wearable devices such as the Apple Watch hold great promise in this regard, as they provide near-continuous monitoring of biometric data. By combining biometric data with demographic, cardiac, and biomarker testing, the investigators will significantly improve our ability to predict heart failure outcomes such as early warning of decompensation, clinical deterioration (symptoms and brain natriuretic peptide (BNP) as a surrogate), hospitalization, mortality (using the Seattle Heart Failure Model (SHFM) as a surrogate), and/or need for advanced heart failure therapies.

Our study has 5 research questions based on 2 primary outcomes and 3 secondary outcomes in clinically diverse adult ambulatory heart failure patients :

Primary Research Question:

Can biometric data obtained from the Apple Watch be used to estimate cardiorespiratory fitness, as assessed by CPET?
Does the 'predicted' Apple 6 MW estimate correlate with formal 6 MWT?

Secondary Research Questions:

Is there a relationship between novel biosensors, including oxygen saturation, and markers of poor prognosis specifically as defined by the SHFM, BNP, Quality of life (QOL) indicators, and CPET parameters?
Can surrogates of cardiorespiratory fitness obtained from the Apple Watch, including novel biosensors, predict acute decompensation of heart failure as defined rapid clinic visits, need for IV diuretics, ED visits, heart failure hospitalization and unscheduled health care encounters during the 3-month follow-up?
Can biometric data be used to improve a risk prediction model that can distinguish between patients at high versus low risk of all-cause hospitalization (primary outcome), all-cause mortality (secondary outcome), and a composite outcome of all-cause mortality, need for ventricular assist device, or heart transplantation (secondary composite outcome) over a 2 year period?

Study Design

Study Type:

Observational

Anticipated Enrollment :

200 participants

Observational Model:

Cohort

Time Perspective:

Prospective

Official Title:

Measuring Cardiopulmonary Fitness in Ambulatory Heart Failure Patients Through the Use of Apple Watch Features

Actual Study Start Date :

Dec 1, 2020

Anticipated Primary Completion Date :

Mar 1, 2022

Anticipated Study Completion Date :

Mar 1, 2022

Outcome Measures

Primary Outcome Measures

Apple Watch metrics prediction of CPET parameters [3 months]
Measure predictive power of Apple Watch metrics such as heart rate against CPET parameters such as peak VO2
6 minute walk test [3 months]
Correlation of Apple 6MW estimate with measured 6MWT

Secondary Outcome Measures

Apple Watch sensors and markers of poor prognosis [3 months]
Correlation between novel biosensors, including oxygen saturation, and markers of poor prognosis specifically as defined by the Seattle Heart Failure Model (SHFM), BNP, Quality of life (QOL) and CPET parameters
Apple Watch Sensors and unscheduled visits [3 months]
Identify whether Apple Watch sensors including estimated peak VO2 predict a composite outcome of unscheduled visits (including: rapid clinic visits, need for IV diuretics, ED visit and HF hospitalization)
Biomarkers and digital signatures as a predictor of composite rehospitalisation, advanced therapies and mortality [2 years]
Identify novel biomarkers (eg. ST2, cell-free DNA, peripheral markers of the microbiome) collected by the biobank and digital signatures assessed by the Apple Watch (eg. behaviors, exercise, medication adherence) that predict a composite outcome of mortality, advanced heart failure therapies and hospitalizations

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years and Older

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

Inclusion Criteria:

broad age range (> 17 years of age)
diverse races/ethnicities,
equal female and male representation,
NYHA functional class I-IV, heart failure with reduced and preserved ejection fraction

Exclusion