Voice Analysis to Detect Pulmonary Arterial Pressure Changes in Heart Failure

VAPP-HF is a prospective, multi-center, observational study assessing whether daily voice recordings analyzed by a machine learning algorithm can detect changes in pulmonary arterial (PA) pressure in heart failure patients with implanted PA pressure sensors (e.g., CardioMEMS, Cordella). Patients across three sites in Germany and the United States provide daily voice recordings via a mobile app for 12 weeks while continuing standard PA pressure monitoring and heart failure care. Voice data is analyzed retrospectively after study completion; no clinical decisions are based on voice analysis during the study. The primary endpoint is the sensitivity and specificity of the AI-based voice analysis in detecting PA pressure changes at defined thresholds.

Implanted PA pressure sensors enable early detection of heart failure decompensation but are costly and invasive. Fluid retention in heart failure may affect the vocal apparatus, producing measurable voice changes that could serve as a non-invasive alternative for monitoring pulmonary congestion.

Participants record daily voice samples consisting of sustained vowel sounds and a standardized reading passage via the Noah Labs mobile app. PA pressure readings are collected daily per standard care. Voice recordings and clinical data are analyzed retrospectively using classical machine learning and deep learning approaches. No additional clinical visits are required.