Open Access
Article
27 June 2025Non-Invasive Evaluation by the HEMOTAGTM Recording Device to Tailor Treatment of Acutely Decompensated Heart Failure
This study evaluated the clinical utility of the HEMOTAG™ recording device—A non-invasive, wearable system that measures cardiac time intervals (CTIs)—in managing patients with acutely decompensated heart failure (ADHF). The prospective, single-center study enrolled 105 patients, including those hospitalized with ADHF and a control group with non-HF-related conditions. Daily measurements of isovolumetric contraction time (IVCT), a key CTI marker, were collected using the HEMOTAG device and compared with NT-proBNP levels obtained on admission and day 3. Among ADHF patients, IVCT decreased in parallel with NT-proBNP levels, indicating volume status improvement with therapy. In contrast, the control group showed no significant change in IVCT or NT-proBNP. An IVCT ≥ 40 ms demonstrated strong sensitivity and specificity to detect ADHF (NT-proBNP ≥ 1800 pg/mL). These findings suggest that IVCT trends measured by HEMOTAG correlate with short-term treatment response in ADHF and could offer a non-invasive method to guide heart failure management. The technology demonstrated feasibility, safety, and clinical relevance, supporting its potential role in future remote management strategies.
Open Access
Article
29 August 2025Physiopathological Insights into Atrial Fibrillation Onset through Heart Rate Variability Correlations
Atrial fibrillation (AF) is the most common
cardiac arrhythmia and is associated with increased morbidity and mortality.
Early prediction of AF episodes remains a clinical challenge. This study aimed
to generate physiopathological hypotheses for AF onset by analyzing
correlations among heart rate variability (HRV) parameters in patients
monitored via long-term Holter ECG. We utilized the IRIDIA-AF database,
comprising 1319 paroxysmal AF episodes from 872 patients. An XGBoost machine
learning model was developed to predict AF onset within 24 h using short- and
long-term HRV features, fragmentation indices, and non-linear metrics extracted
during sinus rhythm. Model interpretation was performed using SHapley Additive
exPlanations (SHAP) values, and dimensionality reduction techniques were
applied for data visualization. The model achieved an area under the receiver
operating characteristic curve of 0.919 and an area under the precision-recall
curve of 0.919, with high accuracy, sensitivity, and specificity. Key
predictive features included short-term vagal activity, HRV fragmentation
indices, and non-linear parameters, highlighting the role of the autonomic
nervous system in AF initiation. Our findings suggest that distinct
physiological profiles, detectable via HRV, may underlie AF susceptibility and
could inform personalized monitoring and prevention strategies.
