DOI: 10.3390/jcm15218441 ISSN: 2077-0383

Device-Derived Digital Biomarkers During SGLT2 Inhibitor Therapy: A Secondary Analysis of the ERASe Trial

Ursula Rohrer, Katharina Ingrid Gölly, Martin Benedikt, Viktoria Santner, Martin Manninger, Simon Leiter, Johannes Widkal, Markus Stühlinger, Lukas Herold, Andreas Zirlik, Harald Sourij, Dirk von Lewinski, Daniel Scherr

Aims: Implantable cardioverter-defibrillators (ICD) and cardiac resynchronization therapy defibrillators (CRT-D) provide continuous physiological data reflecting heart failure (HF) status and autonomic tone. Sodium–glucose cotransporter-2 inhibitors (SGLT2i) are core HF therapy, but it remains unclear whether their physiological effects are detectable using device-based remote monitoring. Following the ERASe trial, which showed reduced ventricular arrhythmias with ertugliflozin, this post-hoc analysis evaluated its impact on ICD/CRT-D-derived parameters. Methods: In this prospective, randomized ERASe post-hoc analysis, ertugliflozin was compared with placebo over 12 months in patients with ICD or CRT-D devices. Device-derived parameters including 24 h heart rate, night heart rate, heart rate variability, physical activity, and thoracic impedance were assessed at baseline and follow-up. Analyses were predefined as exploratory. Results: Forty-six patients (22 ertugliflozin, 24 placebo) had ≥1 analysable parameter. Paired longitudinal data were available for 29 patients for 24 h heart rate (HR), 19 for night HR, 17 for HR variability, and 9 for thoracic impedance. No significant between-group differences were observed in 24 h HR, physical activity or thoracic impedance. No significant differential longitudinal treatment effects were observed for the evaluated device-derived parameters, although estimates were imprecise because of limited parameter-specific data availability. Assessment of associations with arrhythmic and heart-failure outcomes was precluded by small event numbers and incomplete parameter-specific data. Conclusions: Ertugliflozin did not significantly alter individual device-derived parameters. Larger studies are needed to determine whether device-based physiological signals can identify treatment-related changes.

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