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NCT Number: NCT07001007

SCAPIS 2 Cardio - Vibrometer Based Pulse Wave Analysis and CVD Risk Assessment

This clinical investigation will evaluate a novel contactless technology for assessing arterial stiffness and pulse wave characteristics and explore its potential in assessment of coronary artery disease, aortic stenosis and heart failure, in a population-based sample aged 58-72 years.

It will be the first in clinical setting, pilot stage, observational investigation to evaluate the clinical safety, performance and diagnostic accuracy of Cardio P4, a laser-doppler vibrometry (LDV) and microwave radar-based device.

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Key information

Conditions

Coronary Arterial Disease (CAD) Aortic Valve Disease Arterial Stiffness Cardiovascular Diseases Heart Diseases Heart Failure Heart Valve Diseases

Age range

58 year–72 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Danderyds Hospital, KFC - Hjärt-kärllaboratoriet

Stockholm, Sweden

Location status: Recruiting

Location contact

Karin Malmqvist

CONTACT

[email protected]

+46 8 12357184

About this study

Cardiovascular disease (CVD) is the leading cause of death globally, placing a large burden on the healthcare system.

In asymptomatic individuals, there exists several risk scores to predict cardiovascular events (like SCORE2, SCORE2-OP and Framinham Risk Score). In patients with suspected symptoms of coronary artery disease, the European Society of Cardiology advice to estimate the pre-test probability by the risk factor-weighted clinical likelihood (RF-CL).

Most of these scores include the key factors for CAD development including age, sex, LDL- (or non-HDL) cholesterol, diabetes, smoking history, blood pressure or hypertension. Coronary computed tomography angiography (CCTA) is one of the main recommended examinations at low-intermediary risk based on PTP. While the procedure is non-invasive and relatively safe in comparison to invasive alternatives, they still represent a risk to the patient by radiation exposure and incidental findings, and is associated with lack of availability.

One key issue is that once a patient has a CCTA performed showing coronary atherosclerosis, that entails optimized prevention, with solid evidence. However, patients without symptoms of CAD are less studied regarding the degree of CAD. The present SCAPIS trial is the largest such trial on CCTA in 30 000 patients age 50 to 64 years, 25 182 individuals without known coronary heart disease were included. In these asymptomatic persons, CCTA-detected atherosclerosis was found in 42.1% and a significant stenosis (≥50%) in 5.2%.

Arterial stiffness, commonly assessed as pulse wave velocity (PWV), is a marker of aging of the cardiovascular system, and is independently associated with coronary artery disease. Increased arterial stiffness is an early indicator of cardiovascular disease and may improve precision in risk stratification. Laser-Doppler and microwave radar are new promising methods for analysis of pulse waves in human blood vessels. The technology may be more suitable for screening through enabling lower operator dependence and faster assessment time in comparison to standard assessment methods, and may contribute to lower overall healthcare costs and improved precision in identification of CVD.

Analysis of the pulse waveform characteristics such as time intervals and acceleration may in addition to PWV be useful as predictors of risk. We have shown that specific features from the early phase of the waveform (amplitude ratio) are most predictive when analysing similar pressure waveforms captured from the peripheral arteries (by photoplethysmography).

Cardiac timings extracted from a pulse waveform such as left ventricular ejection time and pre-ejection period are independent predictors of diseases such as aortic valve stenosis and heart failure, and may provide an effective method for risk estimation. The most common methods today for diagnosis of cardiac disease (such as aortic stenosis, heart failure) include laboratory tests, assessment of symptoms and echocardiography. Echocardiography is non-invasive, but has high reliance on operator skill which may cause variability in image acquisition and interpretation.

Machine-learning of many waveform features simultaneously, or feature-less analysis using neural networks and language-model driven analysis, may further improve the prediction.

The main aim of the study is to evaluate the potential value of a novel laser-radar-based vibrometer technology that can measure among several features arterial stiffness, and its possible role to improve risk stratification of coronary artery disease.

Secondary aims include to evaluate cardiac timings using laser doppler vibrometry for a possible role to improve risk stratification of patients with aortic stenosis and systolic or diastolic dysfunction.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Subjects already included in the main / general SCAPIS 2 study at Danderyd Hospital in Stockholm, and of which has also participated in the echocardiography examination

Exclusion criteria

  • Patients unable to provide an informed consent

Treatment and study plan

Coronary computer tomography (CCTA)

Diagnostic Test

Use of standard equipment for usual care

Laser Doppler Vibrometry and single-lead ECG

Other

Physiological data acquisition equipment

Echocardiography

Device

GE Vivid E95

Pulse Wave Velocity

Device

Arteriograph, Tensiomed, Hungary

Primary outcomes

  1. Area under receiver operating curve (ROC) to predict CAD-RADS ≥2

    Time frame: Typically within 2 months of enrolment

    Change in area under receiver operating curve (ROC) to predict CAD-RADS ≥2 when adding Vibrometer-based SSN femoral PWV to the standard risk factors.

    Coronary artery disease reporting and data system (CAD-RADS) ≥2 refers to the classification of coronary artery disease with at least moderate stenosis as identified on coronary computer tomography angiography. The classification follows the CAD-RADS 2.0 definition. Stenosis is graded in severity from 0-5.

Secondary outcomes

  1. Area under receiver operating curve (ROC) to predict CAD-RADS ≥3

    Time frame: Typically within 2 months of enrolment

    Change in area under receiver operating curve (ROC) to predict CAD-RADS ≥3 when adding Vibrometer-based SSN femoral PWV to the standard risk factors.

  2. Area under receiver operating curve (ROC) to predict CAD-RADS ≥2

    Time frame: Typically within 2 months of enrolment

    Change in area under receiver operating curve (ROC) to predict CAD-RADS ≥2 when adding Vibrometer-based amplitude-ratio from the early phase of the waveform to the standard risk factors.

  3. Area under receiver operating curve (ROC) to predict CAD-RADS ≥2 stratified by sex

    Time frame: Typically within 2 months of enrolment

    Prespecified subgroup analysis stratified by sex. Change in area under receiver operating curve (ROC) to predict CAD-RADS ≥2 when adding Vibrometer-based SSN femoral PWV to the standard risk factors.

  4. Area under receiver operating curve (ROC) to predict CAD-RADS ≥2 stratified by body mass index strata

    Time frame: Typically within 2 months of enrolment

    Prespecified subgroup analysis stratified by body mass index strata. Change in area under receiver operating curve (ROC) to predict CAD-RADS ≥2 when adding Vibrometer-based SSN femoral PWV to the standard risk factors.

  5. Correlation between vibrometer-based PWA from the suprasternal notch and PWV by Arteriograph

    Time frame: Typically within 2 months of enrolment

    Vibrometer-based PWA (e.g. LVET, PEP) derived from the SSN in relation to PWV acquired by Arteriograph (Tensiomed Inc).

  6. Correlation between vibrometer-based PWA from the suprasternal notch and CAD-RADS score

    Time frame: Typically within 2 months of enrolment

    Vibrometer-based PWA (e.g. LVET, PEP) derived from the SSN in relation to degree of coronary artery disease by CAD-RADS.

  7. Correlation between pressure sensor (piezo) based PWV and CAD-RADS score

    Time frame: Typically within 2 months of enrolment

    Peripheral pressure sensor (piezo) based PWA (such as shape and properties) from sensors on the hands and feet in relation to degree of coronary artery disease by CAD-RADS

  8. Correlation between vibrometer-based PWV and CAC score

    Time frame: Typically within 2 months of enrolment

    Vibrometer-based SSN-femoral PWV in relation to coronary artery calcium (CAC) score.

    The CAC score is a measure of amount of calcified plaque in the coronary arteries, measured using CCTA. The CAC score is associated with increased coronary artery disease risk and future cardiovascular events.

  9. Machine learning analysis of Vibrometer signals

    Time frame: Typically within 2 months of enrolment

    Change in area under receiver operating curve (ROC) to predict CAD-RADS ≥2 when adding machine-learning interpretation of the Vibriometry-signal to models based on traditional risk factors for coronary artery disease.

  10. Correlation between vibrometer-based PWV and left ventricular mass index

    Time frame: Same day as enrolment

    Vibrometer-based SSN-femoral PWV in relation to left ventricular mass index (LVMI), as assessed by echocardiography.

    LVMI is the left ventricular mass indexed by the total body surface area, it has been associated with coronary artery disease events.

  11. Correlation between vibrometer-based left ventricular ejection time and left ventricular ejection fraction

    Time frame: Same day as enrolment

    Vibrometer-based left ventricular ejection time (LVET), derived by quantifying aortic valve opening and closure at the SSN, in relation to left ventricular ejection fraction (LVEF).

  12. Correlation between vibrometer-based PEP/LVET ratio and LVEF

    Time frame: Same day as enrolment

    Ratio of vibrometer-based pre-ejection period (PEP) and LVET in relation to LVEF.

    PEP is the time from depolarization to aortic opening, LVET is the timing from aortic opening to closure. The ratio is associated with cardiovascular outcomes, such as heart failure.

  13. Machine learning analysis of Vibrometer signals

    Time frame: Same day as enrolment

    Machine learning analysis of vibrometry signals to predict LVEF.

  14. Correlation between vibrometer-based LVET and degree of aortic stenosis

    Time frame: Same day as enrolment

    Vibrometer-derived LVET in relation to degree of aortic stenosis, where degree of stenosis is assessed by echocardiography as a score 0-4 (0 = no sign, 4 = severe). The alternative to use DVI ratio instead of this score will also be evaluated.

  15. Correlation between vibrometer-based PEP/LVET ratio and degree of aortic stenosis

    Time frame: Same day as enrolment

    Vibrometer-derived PEP/LVET ratio in relation to degree of aortic stenosis, where degree of stenosis is assessed by echocardiography as a score 0-4 (0 = no sign, 4 = severe). The alternative to use doppler velocity index (DVI) instead of this score will also be evaluated.

    DVI is a doppler echocardiographic index, it is in the context of aortic stenosis calculated as the ratio of maximum velocity across the left ventricular outflow tract (LVOT) and the maximum velocity across the aortic valve obtained by continuous-wave doppler. It is a dimensionless index.

  16. Correlation between vibrometer-based LVET and degree of aortic valve calcification

    Time frame: Same day as enrolment

    Vibrometer-based LVET in relation to degree of aortic valve calcification, where degree of calcification is assessed by X-ray computed tomography (CT) as a standardised score.

  17. Correlation between vibrometer-based PEP/LVET and degree of aortic valve calcification

    Time frame: Same day as enrolment

    Vibrometer-based PEP/LVET ratio in relation to degree of aortic valve calcification, where degree of calcification is assessed by X-ray CT as a standardised score.

  18. Correlation between vibrometer-based LVET and markers of diastolic dysfunction

    Time frame: Same day as enrolment

    Vibrometer-based LVET in relation to diastolic dysfunction, assessed by E/é, left ventricular mass index (LVMI) and left atrial volume (LAVi) from echocardiography.

    E/é is the ratio between early mitral inflow velocity and mitral annular early diastolic velocity.

    LVMI is the mass of the left ventricle indexed by the body surface area.

    LAVi is the left atrial volume indexed by the body surface area.

    All are dimensionless ratios which are associated with severity of diastolic dysfunction.

  19. Correlation between vibrometer-based PEP/LVET markers of diastolic dysfunction

    Time frame: Same day as enrolment

    Vibrometer-based PEP/LVET ratio in relation to diastolic dysfunction, assessed by E/é, LVMI and left atrial volume from echocardiography.

  20. Machine learning analysis of Vibrometer signals

    Time frame: Same day as enrolment

    Machine learning analysis of Vibrometer signals to predict the degree of diastolic dysfunction (grade 1-5).

Study contacts

Contact information is provided by the study sponsor or research team.

Mattias Windå

CONTACT

[email protected]

+46703169040

Sponsors and collaborators

Lead sponsor

HJN Sverige AB/Neko Health

Industry

Registry information

Important dates

Study start
2024
Primary completion
2026
Study completion
2026
First posted
Jun 3, 2025
Registry last updated
Jun 3, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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