Heart FailurePRE-DETECT-HFNCT07443969
Pre-Symptomatic Detection of Impending Decompensation in Heart Failure Through Voice Data
An observational study of Heart Failure and Chronic Heart Failure, run by Noah Labs at 3 sites in 2 countries.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Heart Failure, Chronic Heart Failure, Chronic Heart Disease
- What is being tested?
- Daily Voice Recording and Symptom Monitoring
- How many people
- 123 participants
- Where
- 3 sitesin 2 countries
- Study dates
- 9 Jan 2025to Jun 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Adults aged 18 years or older hospitalized for acute decompensated heart failure or de-novo heart failure, irrespective of left ventricular ejection fraction, recruited from the wards of three participating hospitals in the Netherlands and Spain.
Who may be able to join
- Informed consent provided
- Currently hospitalized for acutely decompensated HF or de-novo HF
- Age: 18 years and above
Who cannot join
- Inability to provide consent
- Pregnancy
- Life-expectancy lower than 1 year due to a condition other than HF
- Planned cardiac intervention within the next 6 months (e.g. valve replacement, bypass surgery)
- Disabling mental diseases (e.g., Alzheimer's disease)
- Symptoms mainly caused by chronic disease other than HF such as chronic obstructive pulmonary disease
Show all 10Show fewer
- Inability to use a smartphone or a tablet computer despite support by informal caregiver if required
- Insufficient knowledge of the local language
- Previous operations on organs involved in generation of voice (vocal tract, vocal folds, etc.)
- Participation in another interventional study within 30 days of inclusion
Trial locations and contacts
3 sites in 2 countries.
3 sites in 2 countries
Netherlands2 sites
Zuyderland Medical Centre
Heerlen, 6419
Maastricht University Medical Centre
Maastricht, 6202AZ
About this study
Official title: Pre-Symptomatic Detection of Impending Decompensation in Heart Failure Through
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.
Read the 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
- Being tested
- Daily Voice Recording and Symptom Monitoring
- Medical subject headings
- Heart Failure
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Daily Voice Recording and Symptom MonitoringOther
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.
| Study group | Role | What they receive |
|---|---|---|
| Voice-Based Monitoring | All participants receive standard heart failure care as per local standard of care plus daily voice monitoring via a mobile application. Patients record voice samples daily and answer symptom questions. Healthcare providers receive symptom-based notifications and may adjust therapy at their discretion. Voice-based risk scores are not used for clinical decisions during the study and are analyzed retrospectively. Receives: Other: Daily Voice Recording and Symptom Monitoring |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity of Voice-Based Software in Detecting Heart Failure Deterioration
Sensitivity of the voice-based prediction in detecting heart failure deterioration, defined as heart failure-related hospitalization, or intensification of heart failure therapy due to worsening heart failure.
Measured: 6 month
Secondary outcomes (5)
Alert Lead Time in Days
Median number of days prior to a heart failure deterioration event that the voice-based algorithm generates an alert, reported in days.
Measured: 6 month
Unexplained Alert Rate per Patient-Year
Number of voice-based alerts not associated with clinical deterioration, reported as a single rate per patient-year of follow-up.
Measured: 6 month
Adherence to voice-based monitoring
Adherence to voice-based monitoring in number and percentage of days with at least one transmitted voice recording.
Measured: 6 month
App Usability via In-App Questionnaires
User experiences, expectations, and acceptance assessed via standardized in-app questionnaires on a 7-point Likert scale.
Measured: 6 month
Quality of Life using the Kansas City Cardiomyopathy Questionnaire
Kansas City Cardiomyopathy Questionnaire (KCCQ) overall summary score (range 0-100, higher scores indicate better health status) at baseline, month 3, and month 6.
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Mar 2026
- Study start9 Jan 2025
- Primary completionJun 2026 (estimated)
- Study completionJun 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Heart FailureStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 123 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Noah Labs
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- Hospital Clinic of Barcelona, Maastricht University, Zuyderland Medical Centre
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07443969
- Sponsor's protocol ID
- 2
- Acronym
- PRE-DETECT-HF
- Status last verified
- Feb 2026
- First submitted
- 25 Jan 2024
- Last updated
- 2 Mar 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 2 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Pre-Symptomatic Detection of Impending Decompensation in Heart Failure Through
- ClinicalTrials.gov ID
- NCT07443969
- Acronym / sponsor ID
- PRE-DETECT-HF / 2
- Conditions
- Heart Failure, Chronic Heart Failure, Chronic Heart Disease
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 123 participants (estimated)
- Lead sponsor
- Noah LabsIndustry (pharma, biotech, devices)
- Collaborators
- Hospital Clinic of Barcelona, Maastricht University, Zuyderland Medical Centre
- Study start
- 9 Jan 2025 (actual)
- Primary completion
- Jun 2026 (estimated)
- Study completion
- Jun 2026 (estimated)
- First posted
- 2 Mar 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Voice-Based Monitoring | – | Other: Daily Voice Recording and Symptom MonitoringAll participants receive standard heart failure care as per local standard of care plus daily voice monitoring via a mobile application. Patients record voice samples daily and answer symptom questions. Healthcare providers receive symptom-based notifications and may adjust therapy at their discretion. Voice-based risk scores are not used for clinical decisions during the study and are analyzed retrospectively. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adults aged 18 years or older hospitalized for acute decompensated heart failure or de-novo heart failure, irrespective of left ventricular ejection fraction, recruited from the wards of three participating hospitals in the Netherlands and Spain.
Key inclusion criteria (3 listed)
- Informed consent provided
- Currently hospitalized for acutely decompensated HF or de-novo HF
- Age: 18 years and above
Key exclusion criteria (10 listed)
- Inability to provide consent
- Pregnancy
- Life-expectancy lower than 1 year due to a condition other than HF
- Planned cardiac intervention within the next 6 months (e.g. valve replacement, bypass surgery)
- Disabling mental diseases (e.g., Alzheimer's disease)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Daily Voice Recording and Symptom MonitoringPatients 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. | Other | Voice-Based Monitoring |
Primary outcome
Sensitivity of Voice-Based Software in Detecting Heart Failure Deterioration
Time frame: 6 month
Sensitivity of the voice-based prediction in detecting heart failure deterioration, defined as heart failure-related hospitalization, or intensification of heart failure therapy due to worsening heart failure.
5 secondary outcomes
Alert Lead Time in Days
Time frame: 6 month
Unexplained Alert Rate per Patient-Year
Time frame: 6 month
Adherence to voice-based monitoring
Time frame: 6 month
App Usability via In-App Questionnaires
Time frame: 6 month
Quality of Life using the Kansas City Cardiomyopathy Questionnaire
Time frame: 6 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Mar 2026.
- Individual participant dataSharing planned: No
Related trials for Heart Failure
RecruitingPhase 4
The Effects of Ferric Derisomaltose in Patients With Acute Heart Failure and Iron Deficiency on Exercise Capacity and Quality of Life(COREVIVE-HFrEF)
Heart Failure With Reduced Ejection Fraction, Iron Deficiency
Not yet recruitingPhase 2
A Study of Bimagrumab in Participants With HFpEF
Heart Failure, Diastolic, Diastolic Heart Failure
Recruiting
Evaluation of a French Hospital Discharge Therapeutic Education Program by Nurses for Heart Failure Patient (EduSor)
Heart Failure
Active, not recruiting
Assessment of Combined CCM and ICD Device in HFrEF
Heart Failure, Heart Failure With Reduced Ejection Fraction
Not yet recruiting
RELIEVE-HFrEF TRIAL: REducing Lung congestIon Symptoms Using the v-wavE Shunt in adVancEd Heart Failure With Reduced EF
Heart Failure and Reduced Ejection Fraction, Heart Failure Chronic
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
3 sites in 2 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Heart Failure and Chronic Heart Failure, run by Noah Labs at 3 sites in 2 countries.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 10 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 2 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in Netherlands, Spain. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity of Voice-Based Software in Detecting Heart Failure Deterioration (time frame: 6 month). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 2 Mar 2026.