NCT07443969

Pre-Symptomatic Detection of Impending Decompensation in Heart Failure Through Voice Data

Pre-Symptomatic Detection of Impending Decompensation in Heart Failure Through

• Status: Active Not Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 123 (estimated)
• Sponsor: Noah Labs · Industry
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

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.

Detailed description

Heart failure decompensation is often detected too late by conventional symptom and weight monitoring, leaving insufficient time to intervene. Invasive alternatives such as implantable pulmonary artery pressure monitors are effective but require surgical implantation. Voice-based digital biomarkers offer a promising non-invasive approach, as fluid overload may produce detectable changes in vocal features. Patients begin voice recordings during hospitalization while still volume overloaded. At home, patients record daily using standardized and variable text content. The voice-based algorithm extracts biomechanical vocal features and calculates a risk score. Healthcare providers access a dashboard showing symptom-based notifications and may adjust therapy at their discretion. Voice-derived risk scores are withheld during the study and analyzed retrospectively. Study visits occur at months 3 and 6 (in-clinic) and month 1 (telephone). Blood samples are collected at baseline, month 3, and month 6 for analysis of traditional (NT-proBNP, creatinine) and novel biomarkers. Usability and quality of life are assessed via questionnaires distributed throughout the study period.

Conditions

• Heart Failure
• Chronic Heart Failure
• Chronic Heart Disease

Interventions

Type	Name	Description
OTHER	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.

Timeline

• Start date: 2025-01-09
• Primary completion: 2026-06-01
• Completion: 2026-06-01
• First posted: 2026-03-02
• Last updated: 2026-03-02

Locations

3 sites across 2 countries: Netherlands, Spain