Coronary Artery DiseaseNCT06734247
Arterial Stiffness for Improved Prediction of Coronary Artery Disease by Coronary CT Angiography
An observational study of Coronary Artery Disease and Chest Pain, run by Danderyd Hospital at 1 site in Sweden.
Trial 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 Read more about this study
- Who is it for?
- Ages 30 to 70 years
- Conditions the study is for
- Coronary Artery Disease, Chest Pain, Dyspnea
- What is being tested?
- Coronary computer tomography angiography (CCTA), Photoplethysmography (PPG) and single-lead ECG and Brachial single cuff-based arterial stiffness
- Type of treatment
- Medical device
- How many people
- 156 participants
- Where
- 1 siteSweden
- Study dates
- 24 Oct 2023to 1 Jul 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 30 to 70 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients undergoing coronary CTA to investigate potential symptomatic coronary artery disease
Who may be able to join
- Patients undergoing coronary computer tomography angiography to investigate stable suspected symptomatic coronary artery disease.
- Age 30 to <70 years of age.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Pulse Wave Velocity and Machine Learning for Prediction of Coronary Artery Disease by Coronary CT Angiography - The Heart Waves Study
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
Read the 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.
- Conditions
- Coronary Artery Disease, Chest Pain, Dyspnea, Arterial Stiffness
- Being tested
- Coronary computer tomography angiography (CCTA), Photoplethysmography (PPG) and single-lead ECG, Brachial single cuff-based arterial stiffness
- Keywords
- PPG, Machine Learning, Pulse Wave Velocity, Arterial Stiffness, CAD-RADS, CACS
- Medical subject headings
- Coronary Artery Disease, Chest Pain, Dyspnea
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Coronary computer tomography angiography (CCTA)Diagnostic test
Use of standard equipment for usual care
Photoplethysmography (PPG) and single-lead ECGOther
Physiological data acquisition equipment
Brachial single cuff-based arterial stiffnessDevice
Use of standard equipment
What is being measured
The primary outcome is the main question the study is designed to answer.
Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥3
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.
Measured: Typically within 1 month of enrollment
Secondary outcomes (9)
Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥2
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.
Measured: Typically within 1 month of enrollment
Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥3 by Arterigraph
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.
Measured: Typically within 1 month of enrollment
Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥2 by Arterigraph
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.
Measured: Typically within 1 month of enrollment
Improved area under receiver operating curve (ROC) to predict Coronary artery calcium score
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.
Measured: Typically within 1 month of enrollment
Number of patients diagnosed with acute or chronic coronary artery disease
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.
Measured: 1 year after enrollment
Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥3 by adding ECG
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]).
Measured: Typically within 1 month of enrollment
Machine learning analysis of photoplethysmography to predict CAD-RADS ≥2
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.
Measured: Typically within 1 month of enrollment
Machine learning analysis of photoplethysmography to predict aortic stenosis
Machine learning analysis of photoplethysmography (PPG) to predict the presense of aortic stenosis (mild-moderate-severe) on cardiac ultrasound.
Measured: Typically within 1 month of enrollment
Machine learning analysis of photoplethysmography to predict cardiac function
Machine learning analysis of photoplethysmography (PPG) to predict systolic and diastolic cardiac function assessed by cardiac ultrasound.
Measured: Typically within 1 month of enrollment
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Dec 2024
- Study start24 Oct 2023
- Primary completion1 Jul 2025
- Study completion1 Jul 2025
Why it stopped: Due to slow recruitment and lack of research staff at Danderyd University Hospital.
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Coronary Artery DiseaseStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 156 participants (actual)
Sponsor and investigators
- Lead sponsor
- Danderyd Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- KTH Royal Institute of Technology
- Responsible party
- Sponsor
- Investigators
- Jonas Spaak, MD, PhD, Principal investigator, Karolinska Institutet, Department of Clinical Sciences, Danderyd Hospital, Division of Cardiovascular Medicine, Stockholm, Sweden
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06734247
- Sponsor's protocol ID
- HW2
- Status last verified
- Apr 2026
- First submitted
- 3 Apr 2024
- Last updated
- 17 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 17 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Pulse Wave Velocity and Machine Learning for Prediction of Coronary Artery Disease by Coronary CT Angiography - The Heart Waves Study
- ClinicalTrials.gov ID
- NCT06734247
- Acronym / sponsor ID
- HW2
- Conditions
- Coronary Artery Disease, Chest Pain, Dyspnea, Arterial Stiffness
- Keywords
- PPG, Machine Learning, Pulse Wave Velocity, Arterial Stiffness, CAD-RADS, CACS
- Study type
- Observational
- Recruitment status
- TerminatedReason stopped: Due to slow recruitment and lack of research staff at Danderyd University Hospital.
- Enrolment
- 156 participants (actual)
- Lead sponsor
- Danderyd HospitalUniversity, hospital or other
- Collaborators
- KTH Royal Institute of Technology
- Study start
- 24 Oct 2023 (actual)
- Primary completion
- 1 Jul 2025 (actual)
- Study completion
- 1 Jul 2025 (actual)
- First posted
- 16 Dec 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 30 to 70 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients undergoing coronary CTA to investigate potential symptomatic coronary artery disease
Key inclusion criteria (2 listed)
- Patients undergoing coronary computer tomography angiography to investigate stable suspected symptomatic coronary artery disease.
- Age 30 to <70 years of age.
Key exclusion criteria (5 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Coronary computer tomography angiography (CCTA)Use of standard equipment for usual care | Diagnostic test | – |
| Photoplethysmography (PPG) and single-lead ECGPhysiological data acquisition equipment | Other | – |
| Brachial single cuff-based arterial stiffnessUse of standard equipment | Device | – |
Primary outcome
Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥3
Time frame: 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.
9 secondary outcomes
Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥2
Time frame: Typically within 1 month of enrollment
Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥3 by Arterigraph
Time frame: Typically within 1 month of enrollment
Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥2 by Arterigraph
Time frame: Typically within 1 month of enrollment
Improved area under receiver operating curve (ROC) to predict Coronary artery calcium score
Time frame: Typically within 1 month of enrollment
Number of patients diagnosed with acute or chronic coronary artery disease
Time frame: 1 year after enrollment
Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥3 by adding ECG
Time frame: Typically within 1 month of enrollment
Machine learning analysis of photoplethysmography to predict CAD-RADS ≥2
Time frame: Typically within 1 month of enrollment
Machine learning analysis of photoplethysmography to predict aortic stenosis
Time frame: Typically within 1 month of enrollment
Machine learning analysis of photoplethysmography to predict cardiac function
Time frame: Typically within 1 month of enrollment
Results and safety
The study was stopped early (reason given: Due to slow recruitment and lack of research staff at Danderyd University Hospital.). Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Apr 2026.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Jonas Spaak, MD, PhD principal investigator
Karolinska Institutet, Department of Clinical Sciences, Danderyd Hospital, Division of Cardiovascular Medicine, Stockholm, Sweden
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Coronary Artery Disease and Chest Pain, run by Danderyd Hospital at 1 site in Sweden.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: stopped early, as of the sponsor's last update on 17 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Sweden. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study was stopped early (reason given: Due to slow recruitment and lack of research staff at Danderyd University Hospital.). Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Improved area under receiver operating curve (ROC) to predict CAD-RADS ≥3 (time frame: Typically within 1 month of enrollment). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Apr 2026.