Clinical trial NCT07001007: SCAPIS 2 Cardio - Vibrometer Based Pulse Wave Analysis and CVD Risk Assessment
Official title: SCAPIS 2 Cardio - Vibrometer Based Pulse Wave Analysis and CVD Risk Assessment
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Coronary Arterial Disease (CAD)
Arterial Stiffness
Aortic Valve Disease
Heart Failure
Lead sponsor: HJN Sverige AB/Neko Health
Sponsor class: INDUSTRY
First posted: 2025-06-03
Enrollment: 1600
Sex: ALL
Minimum age (years): 58
Maximum age (years): 72
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: In SCAPIS (2014 -2018) 30.000 randomly selected adults between the ages of 50 - 64 were included. SCAPIS 2 is the re-assessment study of half of the original study cohort. In Stockholm (Danderyd Hospital site) that corresponds to 2500 adults (that are now between 6-10 years older than the first SCAPIS study). The present study described in this CIP is a prospective, observational substudy of SCAPIS 2, done in collaboration with Karolinska Institutet (KIDS) and Danderyd Hospital.

Brief summary: 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.

Detailed description: 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.

Eligibility criteria: 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

Primary outcomes:
measure: Area under receiver operating curve (ROC) to predict CAD-RADS ≥2
timeFrame: Typically within 2 months of enrolment
Secondary outcomes:
measure: Area under receiver operating curve (ROC) to predict CAD-RADS ≥3
timeFrame: Typically within 2 months of enrolment
measure: Area under receiver operating curve (ROC) to predict CAD-RADS ≥2
timeFrame: Typically within 2 months of enrolment
measure: Area under receiver operating curve (ROC) to predict CAD-RADS ≥2 stratified by sex
timeFrame: Typically within 2 months of enrolment
measure: Area under receiver operating curve (ROC) to predict CAD-RADS ≥2 stratified by body mass index strata
timeFrame: Typically within 2 months of enrolment
measure: Correlation between vibrometer-based PWA from the suprasternal notch and PWV by Arteriograph
timeFrame: Typically within 2 months of enrolment
measure: Correlation between vibrometer-based PWA from the suprasternal notch and CAD-RADS score
timeFrame: Typically within 2 months of enrolment
measure: Correlation between pressure sensor (piezo) based PWV and CAD-RADS score
timeFrame: Typically within 2 months of enrolment
measure: Correlation between vibrometer-based PWV and CAC score
timeFrame: Typically within 2 months of enrolment
measure: Machine learning analysis of Vibrometer signals
timeFrame: Typically within 2 months of enrolment
measure: Correlation between vibrometer-based PWV and left ventricular mass index
timeFrame: Same day as enrolment
measure: Correlation between vibrometer-based left ventricular ejection time and left ventricular ejection fraction
timeFrame: Same day as enrolment
measure: Correlation between vibrometer-based PEP/LVET ratio and LVEF
timeFrame: Same day as enrolment
measure: Machine learning analysis of Vibrometer signals
timeFrame: Same day as enrolment
measure: Correlation between vibrometer-based LVET and degree of aortic stenosis
timeFrame: Same day as enrolment
measure: Correlation between vibrometer-based PEP/LVET ratio and degree of aortic stenosis
timeFrame: Same day as enrolment
measure: Correlation between vibrometer-based LVET and degree of aortic valve calcification
timeFrame: Same day as enrolment
measure: Correlation between vibrometer-based PEP/LVET and degree of aortic valve calcification
timeFrame: Same day as enrolment
measure: Correlation between vibrometer-based LVET and markers of diastolic dysfunction
timeFrame: Same day as enrolment
measure: Correlation between vibrometer-based PEP/LVET markers of diastolic dysfunction
timeFrame: Same day as enrolment
measure: Machine learning analysis of Vibrometer signals
timeFrame: Same day as enrolment
Other outcomes:
Locations:
city: Stockholm
country: Sweden
facility: Danderyds Hospital, KFC - Hjärt-kärllaboratoriet
Study officials:
affiliation: Karolinska Institute, Department of Clinical Sciences, Danderyd Hospital, Division of Cardiovascular Medicine, Stockholm, Sweden
name: Professor Jonas Spaak, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References: