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.