Ted Rogers Understanding of Exacerbations in Heart Failure 2: Monitor

Traditionally, clinicians have relied on static snapshots of patients to determine clinical status and estimate prognosis. More advanced cardiac centres rely on CPET for objective prognosis. There is an unmet need for a more widely available, accessible, and longitudinal assessment of cardiopulmonary fitness and clinical status to better monitor and prognosticate patients. Wearable devices such as Apple Watch hold great promise in this regard, as they provide near-continuous monitoring of biometric data.

In TRUE-HF, we used Apple Watch data to build a novel model for serial daily prediction of cardiopulmonary fitness that is strongly correlated with CPET pVO2. In TRUE-HF2, we seek to prospectively validate the relationship between wearable data and changes in cardiopulmonary fitness, and early warnings of worsening heart failure as measured through decompensation, clinical deterioration, unplanned healthcare utilization, hospitalization, need for advanced heart failure therapies, and mortality.

The goal is to enable equitable access to cardiopulmonary fitness assessment for HF patients who may otherwise face significant barriers to tertiary-centre testing, including travel burden, geography, and limited local resources.

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 Questions:

• Can surrogates of cardiorespiratory fitness estimated from data obtained from Apple Watch in combination with clinical and/or demographical data predict significant reductions in cardiorespiratory fitness in heart failure patients?
• Can surrogates of cardiorespiratory fitness estimated from data obtained from Apple Watch in combination with clinical and/or demographical data predict early warnings of worsening heart failure?

Secondary Research Questions:

• Can biometric data from Apple Watch in combination with clinical and/or demographical data be used to estimate cardiorespiratory fitness measurements and changes, as assessed by CPET?
• Can biometric data from Apple Watch in combination with clinical and/or demographical data from the Apple Watch be used to improve risk prediction models of worsening heart failure as combined (primary) or stratified (secondary) outcomes?
• Can biometric data from Apple Watch in combination with clinical and/or demographical data from the Apple Watch be used to predict markers of poor prognosis specifically as defined by the SHFM, BNP, Quality of life (QOL) indicators, and CPET parameters?

Inclusion criteria

• broad age range (> 18 years of age)
• NT-proBNP > 400 or 1000 if in AF
• Within 3 months post discharge from HF hospitalization/ HF ED visit/ HF rapid clinic visit with intensification of diuretic therapy
• NYHA functional class I-IV, heart failure with reduced and preserved ejection fraction
• Literacy in English
• Patient provided informed consent

Exclusion criteria

• Unable to perform a CPET based on the standard protocol
• End stage renal disease requiring dialysis
• Living with LVAD
• MRP deems patient unfit for the study
• Post heart transplant