Baseline characteristics of patients enrolled in the AZIMUTH trial: an e-health-integrated, smartphone app-based model of care for heart failure patients.

Abstract

Aims: The 'FondAZione A. Gemelli IRCCS Artificial Intelligence Empowered Digital PlatforM to sUpport paTients with Heart Failure' (AZIMUTH) study is a multicentre prospective outpatient trial assessing an app-based, e-health-integrated model of care for heart failure (HF) patients. Its primary aim is to evaluate the feasibility and patient/provider acceptance of the app-based intervention, while key secondary aims include assessing its clinical impact through changes in guideline-directed medical therapy (GDMT) prescription/adherence and quality of life.

Methods and results: Feasibility is defined as ≥70% of participants maintaining active engagement with the app during follow-up. The platform integrates patient-reported symptoms, connected measurements, medication reminders, and two-way messaging, with clinician review of alerts and trends. Between December 2021 and January 2024, 300 adults with chronic HF, irrespective of left ventricular ejection fraction (LVEF), were enrolled across four Italian centres. Median age was 66 years (IQR 59-73), 78.3% were male, and 82.3% were NYHA Class I-II. LVEF distribution was balanced (HFrEF 35.3%, HFmrEF 25.7%, HFpEF 38.7%). Comorbidities were frequent: hypertension 81.3%, dyslipidaemia 75.7%, ischaemic heart disease 41.0%, diabetes 29.7%, atrial fibrillation 29.7%, and chronic kidney disease 21.7%. At enrolment, RASi/ARNI were prescribed in 69.6%, beta-blockers in 76.9%, MRAs in 16.1%, and SGLT2i in 23.7%. Compared with HFrEF-focused trials, uptake of foundational classes was lower, consistent with the broader LVEF spectrum and real-world constraints.

Conclusion: The AZIMUTH cohort represents a contemporary, heterogeneous HF population with variable and suboptimal GDMT uptake. Digital health interventions have the potential to optimize care delivery and increase adherence in real-world settings.