Ignite Creation Date:
2026-03-26 @ 3:17 PM
Ignite Modification Date:
2026-03-31 @ 11:03 AM
Study NCT ID:
NCT07443969
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2026-03-02
First Post:
2024-01-25
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Pre-Symptomatic Detection of Impending Decompensation in Heart Failure Through Voice Data
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}, 'conditionBrowseModule': {'meshes': [{'id': 'D006333', 'term': 'Heart Failure'}], 'ancestors': [{'id': 'D006331', 'term': 'Heart Diseases'}, {'id': 'D002318', 'term': 'Cardiovascular Diseases'}]}}, 'protocolSection': {'designModule': {'bioSpec': {'retention': 'SAMPLES_WITHOUT_DNA', 'description': 'Blood samples collected at baseline, month 3, and month 6. One additional blood vial per draw stored at -70°C to -80°C for batch analysis of traditional and novel heart failure biomarkers.'}, 'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 123}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'ACTIVE_NOT_RECRUITING', 'startDateStruct': {'date': '2025-01-09', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-02', 'completionDateStruct': {'date': '2026-06', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2026-02-24', 'studyFirstSubmitDate': '2024-01-25', 'studyFirstSubmitQcDate': '2026-02-24', 'lastUpdatePostDateStruct': {'date': '2026-03-02', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-03-02', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-06', 'type': 'ESTIMATED'}}, 'outcomesModule': {'otherOutcomes': [{'measure': 'Laboratory results: creatinine, potassium, sodium, urea, NT-proBNP', 'timeFrame': '6 month', 'description': 'Laboratory results: creatinine, potassium, sodium, urea, NT-proBNP'}], 'primaryOutcomes': [{'measure': 'Sensitivity of Voice-Based Software in Detecting Heart Failure Deterioration', 'timeFrame': '6 month', 'description': '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.'}], 'secondaryOutcomes': [{'measure': 'Alert Lead Time in Days', 'timeFrame': '6 month', 'description': 'Median number of days prior to a heart failure deterioration event that the voice-based algorithm generates an alert, reported in days.'}, {'measure': 'Unexplained Alert Rate per Patient-Year', 'timeFrame': '6 month', 'description': 'Number of voice-based alerts not associated with clinical deterioration, reported as a single rate per patient-year of follow-up.'}, {'measure': 'Adherence to voice-based monitoring', 'timeFrame': '6 month', 'description': 'Adherence to voice-based monitoring in number and percentage of days with at least one transmitted voice recording.'}, {'measure': 'App Usability via In-App Questionnaires', 'timeFrame': '6 month', 'description': 'User experiences, expectations, and acceptance assessed via standardized in-app questionnaires on a 7-point Likert scale.'}, {'measure': 'Quality of Life using the Kansas City Cardiomyopathy Questionnaire', 'timeFrame': '6 months', 'description': 'Kansas City Cardiomyopathy Questionnaire (KCCQ) overall summary score (range 0-100, higher scores indicate better health status) at baseline, month 3, and month 6.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Heart Failure', 'Chronic Heart Failure', 'Chronic Heart Disease']}, 'descriptionModule': {'briefSummary': '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.\n\nPatients 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.\n\nThe 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.', 'detailedDescription': '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.\n\nPatients 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.\n\nHealthcare 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.\n\nStudy 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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': '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.', 'healthyVolunteers': False, 'eligibilityCriteria': "Inclusion Criteria:\n\n* Informed consent provided\n* Currently hospitalized for acutely decompensated HF or de-novo HF\n* Age: 18 years and above\n\nExclusion Criteria:\n\n* Inability to provide consent\n* Pregnancy\n* Life-expectancy lower than 1 year due to a condition other than HF\n* Planned cardiac intervention within the next 6 months (e.g. valve replacement, bypass surgery)\n* Disabling mental diseases (e.g., Alzheimer's disease)\n* Symptoms mainly caused by chronic disease other than HF such as chronic obstructive pulmonary disease\n* Inability to use a smartphone or a tablet computer despite support by informal caregiver if required\n* Insufficient knowledge of the local language\n* Previous operations on organs involved in generation of voice (vocal tract, vocal folds, etc.)\n* Participation in another interventional study within 30 days of inclusion"}, 'identificationModule': {'nctId': 'NCT07443969', 'acronym': 'PRE-DETECT-HF', 'briefTitle': 'Pre-Symptomatic Detection of Impending Decompensation in Heart Failure Through Voice Data', 'organization': {'class': 'INDUSTRY', 'fullName': 'Noah Labs'}, 'officialTitle': 'Pre-Symptomatic Detection of Impending Decompensation in Heart Failure Through', 'orgStudyIdInfo': {'id': '2'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Voice-Based Monitoring', 'description': '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.', 'interventionNames': ['Other: Daily Voice Recording and Symptom Monitoring']}], 'interventions': [{'name': 'Daily Voice Recording and Symptom Monitoring', 'type': 'OTHER', 'description': '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.', 'armGroupLabels': ['Voice-Based Monitoring']}]}, 'contactsLocationsModule': {'locations': [{'zip': '6419', 'city': 'Heerlen', 'country': 'Netherlands', 'facility': 'Zuyderland Medical Centre', 'geoPoint': {'lat': 50.88365, 'lon': 5.98154}}, {'zip': '6202AZ', 'city': 'Maastricht', 'country': 'Netherlands', 'facility': 'Maastricht University Medical Centre', 'geoPoint': {'lat': 50.84833, 'lon': 5.68889}}, {'zip': '08036', 'city': 'Barcelona', 'country': 'Spain', 'facility': 'Hospital Clínic de Barcelona', 'geoPoint': {'lat': 41.38879, 'lon': 2.15899}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Noah Labs', 'class': 'INDUSTRY'}, 'collaborators': [{'name': 'Hospital Clinic of Barcelona', 'class': 'OTHER'}, {'name': 'Maastricht University', 'class': 'OTHER'}, {'name': 'Zuyderland Medical Centre', 'class': 'OTHER'}], 'responsibleParty': {'type': 'SPONSOR'}}}}