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Pre-Symptomatic Detection of Impending Decompensation in Heart Failure Through Voice Data
Sponsor: Noah Labs
Summary
PRE-DETECT-HF is a prospective, single-arm observational study evaluating a voice-based machine learning algorithm for early detection of heart failure decompensation. 123 patients hospitalized for acute decompensated or de-novo heart failure will be enrolled across three sites in the Netherlands and Spain. Patients make daily voice recordings via a smartphone app and answer symptom questions for 6 months. The algorithm analyzes voice patterns compared to a baseline recording at discharge. Treatment decisions are based on symptom data only; voice-based predictions are analyzed retrospectively after study completion. The primary endpoint is sensitivity of the voice-based software in detecting heart failure deterioration, defined as heart failure hospitalization, or intensification of heart failure therapy. Secondary endpoints include app adherence, usability, and associations between voice data and blood biomarkers.
Official title: Pre-Symptomatic Detection of Impending Decompensation in Heart Failure Through
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
OBSERVATIONAL
Enrollment
123
Start Date
2025-01-09
Completion Date
2026-06
Last Updated
2026-03-02
Healthy Volunteers
No
Interventions
Daily Voice Recording and Symptom Monitoring
Patients use the mobile app daily to record voice samples and answer symptom-related questions. Voice recordings are analyzed by a algorithm, which extracts vocal biomechanical features. Healthcare providers receive notifications based on symptom data only and may adjust therapy at their discretion. Voice-derived risk scores are not shared with clinicians during the study and are analyzed retrospectively after study completion.
Locations (3)
Zuyderland Medical Centre
Heerlen, Netherlands
Maastricht University Medical Centre
Maastricht, Netherlands
Hospital Clínic de Barcelona
Barcelona, Spain