Clinical trial NCT06734247: Arterial Stiffness for Improved Prediction of Coronary Artery Disease by Coronary CT Angiography
Official title: Pulse Wave Velocity and Machine Learning for Prediction of Coronary Artery Disease by Coronary CT Angiography - the Heart Waves Study
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Coronary Artery Disease
Chest Pain
Dyspnea
Arterial Stiffness
Keywords: PPG
Machine Learning
Pulse Wave Velocity
Arterial Stiffness
CAD-RADS
CACS
Lead sponsor: Danderyd Hospital
Sponsor class: OTHER
Collaborators: name: KTH Royal Institute of Technology
First posted: 2024-12-16
Enrollment: 200
Sex: ALL
Minimum age (years): 30
Maximum age (years): 70
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients undergoing coronary CTA to investigate potential symptomatic coronary artery disease

Brief summary: This study will evaluate the ability of device-estimated pulse wave velocity and machine learning methods to improve the prediction of potential symptomatic coronary artery disease

Detailed description: In stable patients with suspected symptomatic coronary artery disease, an estimation of pre-test probability (PTP) and a clinical assessment are used to decide who should be investigated further. PTP has historically been based on age, sex, the nature of chest pain or dyspnea as angina equivalent. It is recommended to continue investigation of all with PTP ≥15%, but also to consider investigation at PTP 5-15% (low-intermediate risk) which is the majority of patients. Despite updates to PTP estimations in the 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes, it has been shown that they overestimate the risk of coronary artery disease.

In 2024, the ESC Guidelines were updated to recommend an updated clinical assessment method, the risk factor-weighted clinical likelihood (RF-CL), which is based on symtoms and number of risk factors. It has been shown to have better predictive ability compared to PTP alone, but is still largely based on epidemiological data, which may not be valid for all individuals.

Coronary computer tomography angiography (CCTA) is the method becoming increasingly established at low-intermediate risk. An initial, non-invasive strategy with CCTA compared to invasive or more advanced examinations is safe and simple. At the same time, CCTA is resource-intensive, with limited availability, and the examination involves both contrast, radiation and incidental findings. Thus, there is a need to improve the risk estimation.

Arterial stiffness assessed by pulse wave velocity is an independent marker for cardiovascular events and has been shown to be independently associated with the degree of coronary artery disease. Arterial stiffness is, however, rarely measured in the clinic as it traditionally has required cumbersome procedures. Newer methods include the brachial single cuff-based Arteriograph and the optical technique photoplethysmography (PPG), widely available in healthcare pulse oximeters, but increasingly also in different consumer devices, often complemented by single-lead ECG.

The main aim of this study is to evaluate arterial stiffness and its possible role to improve risk stratification of patients undergoing CCTA for potential coronary artery disease.

Eligibility criteria: Inclusion Criteria:

* Patients undergoing coronary computer tomography angiography to investigate stable suspected symptomatic coronary artery disease.
* Age 30 to \<70 years of age.

Exclusion Criteria:

* Known coronary artery disease (prior myocardial infarction, percutaneous coronary intervention, coronary artery bypass graft or any angiographic evidence of coronary artery disease ≥50% lesion in a major epicardial vessel).
* Known significant cardiac (\> moderate valvular disease, heart failure with reduced ejection fraction, hypertrophic cardiomyopathy, or congenital heart disease), or pulmonary condition which could explain symptoms.
* Known ongoing atrial fibrillation/flutter.
* No Swedish social security number.
* Unable to provide written informed consent.

Primary outcomes:
measure: Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥3
timeFrame: Typically within 1 month of enrollment
Secondary outcomes:
measure: Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥2
timeFrame: Typically within 1 month of enrollment
measure: Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥3 by Arterigraph
timeFrame: Typically within 1 month of enrollment
measure: Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥2 by Arterigraph
timeFrame: Typically within 1 month of enrollment
measure: Improved area under receiver operating curve (ROC) to predict Coronary artery calcium score
timeFrame: Typically within 1 month of enrollment
measure: Number of patients diagnosed with acute or chronic coronary artery disease
timeFrame: 1 year after enrollment
measure: Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥3 by adding ECG
timeFrame: Typically within 1 month of enrollment
measure: Machine learning analysis of photoplethysmography to predict CAD-RADS ≥2
timeFrame: Typically within 1 month of enrollment
measure: Machine learning analysis of photoplethysmography to predict aortic stenosis
timeFrame: Typically within 1 month of enrollment
measure: Machine learning analysis of photoplethysmography to predict cardiac function
timeFrame: Typically within 1 month of enrollment
Other outcomes:
Locations:
city: Stockholm
country: Sweden
facility: Danderyd University Hospital
Study officials:
affiliation: Karolinska Institutet, Department of Clinical Sciences, Danderyd Hospital, Division of Cardiovascular Medicine, Stockholm, Sweden
name: Jonas Spaak, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References: