Ignite Creation Date:
2025-12-24 @ 4:54 PM
Ignite Modification Date:
2026-01-02 @ 1:29 AM
Study NCT ID:
NCT07284550
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-12-16
First Post:
2025-11-14
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Smartphone Based Digital Screening for Aortic Valve Stenosis
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D001024', 'term': 'Aortic Valve Stenosis'}], 'ancestors': [{'id': 'D000082862', 'term': 'Aortic Valve Disease'}, {'id': 'D006349', 'term': 'Heart Valve Diseases'}, {'id': 'D006331', 'term': 'Heart Diseases'}, {'id': 'D002318', 'term': 'Cardiovascular Diseases'}, {'id': 'D014694', 'term': 'Ventricular Outflow Obstruction'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 500}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-12', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-10', 'completionDateStruct': {'date': '2029-11', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-12-02', 'studyFirstSubmitDate': '2025-11-14', 'studyFirstSubmitQcDate': '2025-12-02', 'lastUpdatePostDateStruct': {'date': '2025-12-16', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-12-16', 'type': 'ESTIMATED'}, 'primaryCompletionDateStruct': {'date': '2028-11', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Sensitivity and specificity of a smartphone-derived algorithm for detecting moderate-to-severe aortic stenosis (AVA ≤ 1.5 cm²), using echocardiography as the reference standard', 'timeFrame': 'At the baseline study visit (after completion of smartphone and echocardiographic assessments)', 'description': 'Sensitivity and specificity will be calculated by comparing the classification produced by the smartphone-based algorithm with the diagnosis obtained from transthoracic echocardiography, which serves as the clinical reference standard. Aortic stenosis severity will be defined according to established guideline criteria, with moderate-to-severe aortic stenosis classified as an aortic valve area (AVA) of ≤ 1.5 cm². Smartphone recordings will be obtained during a single study visit using built-in microphones and motion sensors to capture heart sounds and chest wall vibrations. Echocardiographic measurements, performed by certified clinical personnel, will provide the comparator classification. The reported outcome will reflect how accurately the smartphone algorithm identifies participants with moderate-to-severe aortic stenosis at this time point.'}], 'secondaryOutcomes': [{'measure': 'Quality of smartphone-acquired cardiac signals, measured by signal-to-noise ratio (SNR)', 'timeFrame': 'At the baseline study visit', 'description': 'Signal quality will be quantified by calculating the signal-to-noise ratio (SNR) of heart sound and vibration recordings captured using built-in smartphone microphones and motion sensors during the study visit. Higher SNR values indicate clearer cardiac signals with less background noise. The reported outcome reflects the feasibility and technical performance of the smartphone recording pipeline.'}, {'measure': "Agreement between smartphone-derived aortic stenosis classification and echocardiographic grading, measured by Cohen's kappa coefficient", 'timeFrame': 'At the baseline study visit', 'description': "Agreement between the severity classification produced by the smartphone-based algorithm and the clinical reference standard (echocardiographic grading of aortic stenosis) will be quantified using Cohen's kappa coefficient. Echocardiographic classification will follow guideline-based severity thresholds. The reported value reflects the degree of concordance between both methods beyond chance."}, {'measure': 'Area under the receiver operating characteristic curve (AUROC) of the smartphone-based algorithm for detecting moderate-to-severe aortic stenosis', 'timeFrame': 'At the baseline study visit', 'description': 'The AUROC will be calculated to assess the discriminatory ability of the smartphone-based algorithm to distinguish between participants with and without moderate-to-severe aortic stenosis, as defined by an aortic valve area (AVA) ≤ 1.5 cm² on echocardiography. Higher AUROC values indicate better diagnostic performance.'}, {'measure': 'Incidence of major adverse cardiac and cerebrovascular events (MACCE)', 'timeFrame': 'Up to 12 months after the baseline study visit', 'description': 'Major adverse cardiac and cerebrovascular events (MACCE)-including all-cause mortality, cardiovascular mortality, non-fatal myocardial infarction, non-fatal stroke, and hospitalization for heart failure-will be recorded during follow-up. The clinical event data will be analyzed in relation to cardiac signal characteristics extracted from the baseline smartphone recordings (e.g., murmur intensity, dominant frequency patterns, signal-to-noise ratio). This outcome explores whether smartphone-derived cardiac features are associated with subsequent adverse clinical events.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Aortic Valve Stenosis', 'Artifical Intelligence']}, 'descriptionModule': {'briefSummary': 'Heart valve diseases are among the most serious cardiovascular conditions in older age. One of the most common forms is aortic valve stenosis, a narrowing of the valve opening between the left ventricle and the main artery. As the valve becomes tighter, the heart must work harder and harder to pump blood through the body. This process often develops slowly over many years and initially causes no clear symptoms. As a result, the condition is frequently detected only in advanced stages, when warning signs such as shortness of breath, chest pain, or dizziness appear. Without treatment, aortic valve stenosis can become life-threatening. If detected early, however, very effective treatment options are available today.\n\nUp to now, the disease has been reliably diagnosed mainly through echocardiography. Yet this method is complex, costly, and requires specialized medical staff. A simple, affordable, and broadly accessible screening option does not yet exist.\n\nThe interdisciplinary clinical research project explores whether conventional smartphones could fill this gap. Almost all modern devices are equipped with sensors such as microphones, accelerometers, and gyroscopes. These can capture both heart sounds and subtle vibrations of the chest. The research team is investigating whether reliable diagnostic information for the diagnosis of aortic valve stenosis can be extracted from such recordings. To achieve this, the signals are processed with newly developed methods and analyzed using artificial intelligence.\n\nFor the study, several hundred patients with and without valve disease will be examined. The smartphone results will be compared with established diagnostic standards, particularly echocardiography, to test accuracy and reliability.\n\nIf successful, the approach could enable a straightforward, digital heart check at home using nothing more than a conventional smartphone. Such a tool would provide an accessible, low-cost, and widely available method for early detection, helping more people receive timely and potentially life-saving treatment.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'The SMART-VALVE project is a single-centre, proof-of-concept study. The study will be conducted in 2 stages. In stage 1, data will be collected to develop and validate an ML-based aortic stenosis classification algorithm. In stage 2, the developed algorithm is tested against newly acquired data from previously unseen participants. In stage 1, a total of 300 participants will be recruited for training and validation from clinical populations with moderate-to severe AS (group I) and a control group without significant Valvular Heart Disease (group II). Individuals in the control group will be matched to the AS patient group based on age, gender, and BMI (see Figure 5).\n\nThe collected sensor data will be analysed to extract and engineer features and identify potential digital biomarkers indicative of aortic stenosis. AI algorithms will be applied to these datasets to develop predictive models for the classification of AS patients and individuals based on the recorded si', 'healthyVolunteers': True, 'eligibilityCriteria': 'The following inclusion and exclusion criteria will be used for training, validation and test sets:\n\nInclusion criteria for group I (moderate to severe AS):\n\n* Moderate to severe AS defined as AVA ≤ 1.5cm² in echocardiographic assessment\n* No other significant VHD, valvular prosthesis, pacemaker or congenital heart defect\n* Documented echocardiography as part of routine clinical practice no older than 90 days\n* Patient age ≥ 18 years\n* Provided written informed consent\n\nInclusion criteria for group II:\n\n* No significant VHD, valvular prosthesis, pacemaker or congenital heart defect\n* Documented echocardiography as part of routine clinical practice no older than 90 days\n* Patient age ≥ 18 years\n* Provided written informed consent\n\nExclusion criteria (applicable for all groups):\n\n• Informed consent form not signed.'}, 'identificationModule': {'nctId': 'NCT07284550', 'acronym': 'SMART-VALVE', 'briefTitle': 'Smartphone Based Digital Screening for Aortic Valve Stenosis', 'organization': {'class': 'OTHER', 'fullName': 'Medical University Innsbruck'}, 'officialTitle': 'Smartphone Based Digital Screening for Aortic Valve Stenosis', 'orgStudyIdInfo': {'id': '1328/2020_1'}}, 'armsInterventionsModule': {'interventions': [{'name': 'Smartphone-based signal acquisition', 'type': 'DIAGNOSTIC_TEST', 'description': 'To enable the study, we have already developed a pipeline from smartphone-based signal acquisition to secure signal upload. This will be followed by analysis of the microphone, accelerometer and gyroscope data and development of algorithms based on to-be-defined signal features.'}]}, 'contactsLocationsModule': {'centralContacts': [{'name': 'Michael Schreinlechner, MD', 'role': 'CONTACT', 'email': 'Michael.Schreinlechner@i-med.ac.at', 'phone': '+4351250425621'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Medical University Innsbruck', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}