Congestion assessment using a self-supervised contrastive learning-derived risk index in patients with congestive...

Recurrent congestive episodes are a primary cause of hospitalizations in patients with heart failure. Hitherto, outpatient management adopts a reactive approach, assessing patients clinically through frequent follow-up visits to detect congestion early. This study aims to assess the capabilities of a self-supervised contrastive learning-derived risk index to detect episodes of acute decompensated heart failure (ADHF) in patients using continuously recorded wearable time-series data.

This is the protocol for a single-arm, prospective cohort pilot study that will include 290 patients with ADHF. Acute decompensated heart failure is diagnosed by clinical signs and symptoms, as well as additional diagnostics (e.g. NT-proBNP). Patients will receive standard-of-care treatment, supplemented by continuous wearable-based monitoring of vital signs and physical activity, and are followed for 90 days. During follow-up, study visits will be conducted and presentations without clinical ADHF will be referred to as 'regular' and data from these episodes will be presented to a deep neural network that is trained by a self-supervised contrastive learning objective to extract features from the time-series that are typical in regular periods. The model is used to calculate a risk index measuring the dissimilarity of observed features from those of regular periods. The primary outcome of this study will be the risk index's accuracy in detecting episodes with ADHF. As secondary outcome data integrity and the score in the validated questionnaire System Usability Scale will be evaluated.

Demonstrating reliable congestion detection through continuous monitoring with a wearable and self-supervised contrastive learning could assist in pre-emptive heart failure management in clinical care.

The study was registered in the German clinical trials register (DRKS00034502).