Arterial Stiffness, Chest Pain, Coronary Artery Disease, Dyspnea
Conditions
Keywords
PPG, Machine Learning, Pulse Wave Velocity, Arterial Stiffness, CAD-RADS, CACS
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.
Interventions
DIAGNOSTIC_TESTCoronary computer tomography angiography (CCTA)
Use of standard equipment for usual care
OTHERPhotoplethysmography (PPG) and single-lead ECG
Physiological data acquisition equipment
DEVICEBrachial single cuff-based arterial stiffness
Use of standard equipment
Sponsors
Danderyd Hospital
KTH Royal Institute of Technology
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
30 Years to 70 Years
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥3 | Typically within 1 month of enrollment | Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥3 when adding photoplethysmography (PPG) estimated arterial stiffness to the standard model (including age, sex, symtom score \[0-3\] and number of risk factors \[0-5\]). Coronary artery disease reporting and data system (CAD-RADS) ≥3 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
| Measure | Time frame | Description |
|---|---|---|
| Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥2 | Typically within 1 month of enrollment | Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥2 when adding photoplethysmography (PPG) estimated arterial stiffness to models with traditional risk factors for coronary artery disease. Coronary artery disease reporting and data system (CAD-RADS) ≥2 refers to the classification of coronary artery disease with at least mild 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. |
| Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥3 by Arterigraph | Typically within 1 month of enrollment | Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥3 when adding Arteriograph-estimated arterial stiffness to the standard model (including age, sex, symtom score \[0-3\] and number of risk factors \[0-5\]). Coronary artery disease reporting and data system (CAD-RADS) ≥3 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. |
| Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥2 by Arterigraph | Typically within 1 month of enrollment | Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥2 when adding Arteriograph-estimated arterial stiffness to models with traditional risk factors for coronary artery disease. Coronary artery disease reporting and data system (CAD-RADS) ≥2 refers to the classification of coronary artery disease with at least mild 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. |
| Improved area under receiver operating curve (ROC) to predict Coronary artery calcium score | Typically within 1 month of enrollment | Improved area under receiver operating curve (ROC) to predict coronary artery calcium (CAC) score when adding photoplethysmography (PPG) or Arteriograph estimated arterial stiffness to models with traditional risk factors for coronary artery disease. PPG-ECG signals used in machine learning and advanced modelling may further improve the prediction. The coronary artery calcium (CAC) score is a measure of the amount of calcified plaque in the coronary arteries, as identified on coronary computer tomography angiography. Higher CAC scores are associated with increased risk of coronary artery disease and future cardiovascular events. |
| Number of patients diagnosed with acute or chronic coronary artery disease | 1 year after enrollment | As safety outcome; proportion of those who our model estimated as low risk and then diagnosed with acute or chronic coronary artery disease in the year following inclusion in the study. |
| Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥3 by adding ECG | Typically within 1 month of enrollment | Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥2 when adding estimated arterial stiffness and machine-learning interpretation of ECG to the standard model (including age, sex, symtom score \[0-3\] and number of risk factors \[0-5\]). |
| Machine learning analysis of photoplethysmography to predict CAD-RADS ≥2 | Typically within 1 month of enrollment | Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥2 when adding machine-learning interpretation of the photoplethysmography (PPG) signal to models based on traditional risk factors for coronary artery disease. |
| Machine learning analysis of photoplethysmography to predict aortic stenosis | Typically within 1 month of enrollment | Machine learning analysis of photoplethysmography (PPG) to predict the presense of aortic stenosis (mild-moderate-severe) on cardiac ultrasound. |
| Machine learning analysis of photoplethysmography to predict cardiac function | Typically within 1 month of enrollment | Machine learning analysis of photoplethysmography (PPG) to predict systolic and diastolic cardiac function assessed by cardiac ultrasound. |
Countries
Sweden
Contacts
PRINCIPAL_INVESTIGATORJonas Spaak, MD, PhD
Karolinska Institutet, Department of Clinical Sciences, Danderyd Hospital, Division of Cardiovascular Medicine, Stockholm, Sweden
Outcome results
None listed