Coronary Artery DiseaseAiPHDNCT06029387
Artificial Intelligence for Preventing Heart Disease (AiPHD): Observational, Single Center, Prospective and Retrospective Study
An observational study of Coronary Artery Disease, run by IRCCS San Raffaele at 1 site in Italy.
Trial summary
Coronary artery disease (CAD) is a leading cause of mortality in western countries. Coronary computed tomography angiography (cCTA) is the first-line imaging test in patients with suspected obstructive CAD. However, in most patients, cCTA shows non-obstructive CAD. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Coronary Artery Disease
- How many people
- 3,000 participants
- Where
- 1 siteItaly
- Study dates
- 31 Jul 2023to 14 Nov 2024
- 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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients undergoing clinically indicated cCTA for suspected CAD
Who may be able to join
- Patients with cCTA performed for CAD assessment
Who cannot join
- Refusal to participate in the study
- Age <18 years old
- Presence of other cardiovascular comorbidities (e.g. severe valvulopathies; non-ischemic cardiomyopathies; etc.)
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Artificial Intelligence for Preventing Heart Disease: Observational, Single Center, Prospective and Retrospective Study
Coronary artery disease (CAD) is a leading cause of mortality in western countries. Coronary computed tomography angiography (cCTA) is the first-line imaging test in patients with suspected obstructive CAD. However, in most patients, cCTA shows non-obstructive CAD. The management of patients with non-obstructive CAD is unclear. This is due to the lack of cCTA-based methods capable to assess the risk of disease progression towards developing major adverse cardiovascular events (MACEs) based on the atherosclerosis characteristics of each patient. A solution for prognostication in these patients is particularly appealing since it could allow to identify patients who can benefit of a more aggressive medical treatment and management, thus improving outcome.
Proposed methods, which include qualitative evaluations such as the identification of adverse atherosclerotic plaque characteristics or quantitative evaluations such as the quantification of atherosclerotic plaque burden, may in some cases suffer of limited reproducibility between operators and software. Most importantly, each single biomarker is insufficient to accurately predict patient risk, hence potential synergic integration of cCTA and clinical biomarkers is the key to efficiently guide the personalization of patient's management. Furthermore, the few risk stratification methods that have been proposed are not designed to work on platforms capable of deploying the solution to other clinical settings, promoting prospective or external validation
Read the detailed description
Background and Rationale
In recent years, cCTA has become a crucial diagnostic tool for suspected CAD, recommended as the first-line test for patients with an intermediate pre-test probability of CAD by the European Society of Cardiology (ESC) guidelines and the American HeartAssociation (AHA) and Italian guidelines , because of the well-known high negative predictive value of cCTA in ruling out obstructive CAD. However, most patients have non-obstructive CAD.
While the management of patients with obstructive CAD is established, as it revolves around further diagnostic test for ischemia evaluation or upfront coronary artery revascularization, this is not the case for patients with non-obstructive CAD. However, these cohort of patients still has a significant risk of developing major adverse cardiovascular events (MACEs) that could be prevented by implementing adequate medical therapy.
To date, many approaches have been proposed to tackle this issue. However, these proposed solutions lack the ability to provide quantitative and reproducible results with a sufficiently strong predictive value, are often proposed as a stand-alone solution without the integration with multiple prognosticator imaging and clinical parameters, and are not delivered through platforms capable of providing external validation and easy integration in the clinical workflow. Among the proposed prognostic approaches, some are based on the qualitative evaluation of coronary artery plaque features, such as positive remodeling, low attenuation of the plaque, presence of spotty calcification, and "napkin ring" sign , which is subject to significant inter-reader variability.
Other approaches rely on quantitative methods for evaluating atherosclerotic burden based on the extent of coronary artery plaques and their characteristics, such as calcium density, number of lesions, regional distribution, plaque volume, non-calcified plaque volume etc.
However, these approaches may be hampered by low reproducibility, especially among different scanner vendors. Interestingly, a new research has also shown that, besides coronary artery vessel wall characteristics, pericoronary adipose tissue attenuation carries significant predictive value, as it reflects the state of coronary inflammation that plays a key role in the development and progression of coronary atherosclerosis.
All these CAD characteristics are often analyzed independently from one to another, reducing their potential synergistic prognostic value and creating redundant variables that have negligible effect on prognosis. We propose an AI-based analysis that can integrate all this data in order to select the most important determinant of CAD progression and to discard futile features, thus creating an agile and clinically valuable risk stratification model.Furthermore, we plan to create a novel imaging marker of CAD with unfavorable outcome, to be integrated in the AI-based model, which will be based on topological features of the coronary artery tree. In fact, data on the association between coronary artery topology (e.g., vessel-length, coronary artery volume index, cross-sectional area, curvature, and tortuositv) and prognosis is scarce. However, it is known that vessel tortuosity influences wall shear stress and leads to disruption of laminar flow, resulting in endothelial dysfunction and flow alterations that may lead to atherosclerosis, eventually causing adverse cardiac events . Thus, this novel biomarker may carry a significant prognostic role. Based on these premises, our research aims to develop a novel clinical-imaging AI-based model to identify and categorize patients at high risk of disease progression and provide a more personalized management approach to improve patient outcomes.
However, besides the primary objective of creating an AI-based model for CAD risk stratification, we aim to overcome some issues that currently hamper the widespread clinical application of AI in cardiovascular care. In fact, it is recognized that the integration of AI-based applications into the clinical workflow, which will increase usability and decrease costs, is currently lacking.
We aim to tackle these issues with the help of the industrial partners involved in this project that will build a platform capable of delivering the software solution to provide external validation of the algorithm.
This platform will be characterized by state-of-the-art security measures, interoperability with current clinical software, and easy-to-use interface.
- Conditions
- Coronary Artery Disease
- Keywords
- Coronary Computed Tomography Angiography, Major adverse cardiovascular events, Artificial Intelligence, Proportional Hazards
- Medical subject headings
- Coronary Artery Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Retrospective cohort | The retrospective cohort will include 2500 patients who underwent a clinically indicated cCTA examination for CAD evaluation. Duration of enrollment: enrollment of patients via a telephone call will last 15 months starting from the beginning of the study (month 0). Duration of total follow-up: no follow-up is planned. Duration of total study period: total retrospective study duration will be 30 months | |
| Prospective cohort | The prospective cohort will include 500 patients undergoing a clinically indicated cCTA for CAD evaluation. Duration of enrollment: enrollment of patients at the time of cCTA examination will last 12 months. Duration of total follow-up: each patient will be followed up for 36 months from the date of the cCTA. Duration of total study period: total prospective study duration will be 54 months (last patient enrolled at month 12 + 36 months of follow-up + 6 months for data analysis). |
What is being measured
The primary outcome is the main question the study is designed to answer.
composite outcome 1
death by cardiovascular events or non-fatal myocardial infarction
Measured: 36 months from CCTA examination
Secondary outcomes (1)
composite outcome 2
all cause mortality, non-fatal myocardial infarction, hospitalization due to angina or angina-like symptoms and late coronary revascularization
Measured: 36 months from CCTA examination
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Sep 2023
- Study start31 Jul 2023
- Primary completion14 Nov 2024
- Study completion14 Nov 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (17)Papers the sponsor has linked to this study.
- 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
- Other
- Enrolment
- 3,000 participants (actual)
Sponsor and investigators
- Lead sponsor
- IRCCS San Raffaele
- Sponsor type
- University, hospital or other
- Collaborators
- DGS, EBIT, Dyrecta Lab, PORINI
- Responsible party
- Principal investigator, Antonio Esposito, IRCCS San Raffaele
- Investigators
- Antonio Esposito, Principal investigator, IRCCS San Raffaele
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06029387
- Sponsor's protocol ID
- AiPHD F/310003/01/X56
- Acronym
- AiPHD
- Status last verified
- Dec 2025
- First submitted
- 1 Sep 2023
- Last updated
- 15 Jan 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Knuuti J, Wijns W, Saraste A, Capodanno D, Barbato E, Funck-Brentano C, Prescott E, Storey RF, Deaton C, Cuisset T, Agewall S, Dickstein K, Edvardsen T, Escaned J, Gersh BJ, Svitil P, Gilard M, Hasdai D, Hatala R, Mahfoud F, Masip J, Muneretto C, Valgimigli M, Achenbach S, Bax JJ; ESC Scientific Document Group. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J. 2020 Jan 14;41(3):407-477. doi: 10.1093/eurheartj/ehz425. No abstract available.
PubMed 31504439Writing Committee Members; Gulati M, Levy PD, Mukherjee D, Amsterdam E, Bhatt DL, Birtcher KK, Blankstein R, Boyd J, Bullock-Palmer RP, Conejo T, Diercks DB, Gentile F, Greenwood JP, Hess EP, Hollenberg SM, Jaber WA, Jneid H, Joglar JA, Morrow DA, O'Connor RE, Ross MA, Shaw LJ. 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol. 2021 Nov 30;78(22):e187-e285. doi: 10.1016/j.jacc.2021.07.053. Epub 2021 Oct 28.
PubMed 34756653Esposito A, Francone M, Andreini D, Buffa V, Cademartiri F, Carbone I, Clemente A, Guaricci AI, Guglielmo M, Indolfi C, La Grutta L, Ligabue G, Liguori C, Mercuro G, Mushtaq S, Neglia D, Palmisano A, Sciagra R, Seitun S, Vignale D, Pontone G, Carrabba N. SIRM-SIC appropriateness criteria for the use of Cardiac Computed Tomography. Part 1: Congenital heart diseases, primary prevention, risk assessment before surgery, suspected CAD in symptomatic patients, plaque and epicardial adipose tissue characterization, and functional assessment of stenosis. Radiol Med. 2021 Sep;126(9):1236-1248. doi: 10.1007/s11547-021-01378-0. Epub 2021 Jun 23.
PubMed 34160775Conte E, Annoni A, Pontone G, Mushtaq S, Guglielmo M, Baggiano A, Volpato V, Agalbato C, Bonomi A, Veglia F, Formenti A, Fiorentini C, Bartorelli AL, Pepi M, Andreini D. Evaluation of coronary plaque characteristics with coronary computed tomography angiography in patients with non-obstructive coronary artery disease: a long-term follow-up study. Eur Heart J Cardiovasc Imaging. 2017 Oct 1;18(10):1170-1178. doi: 10.1093/ehjci/jew200.
PubMed 27679600Otsuka K, Fukuda S, Tanaka A, Nakanishi K, Taguchi H, Yoshikawa J, Shimada K, Yoshiyama M. Napkin-ring sign on coronary CT angiography for the prediction of acute coronary syndrome. JACC Cardiovasc Imaging. 2013 Apr;6(4):448-57. doi: 10.1016/j.jcmg.2012.09.016. Epub 2013 Mar 14.
PubMed 23498679Nerlekar N, Ha FJ, Cheshire C, Rashid H, Cameron JD, Wong DT, Seneviratne S, Brown AJ. Computed Tomographic Coronary Angiography-Derived Plaque Characteristics Predict Major Adverse Cardiovascular Events: A Systematic Review and Meta-Analysis. Circ Cardiovasc Imaging. 2018 Jan;11(1):e006973. doi: 10.1161/CIRCIMAGING.117.006973.
PubMed 29305348Maroules CD, Hamilton-Craig C, Branch K, Lee J, Cury RC, Maurovich-Horvat P, Rubinshtein R, Thomas D, Williams M, Guo Y, Cury RC. Coronary artery disease reporting and data system (CAD-RADSTM): Inter-observer agreement for assessment categories and modifiers. J Cardiovasc Comput Tomogr. 2018 Mar-Apr;12(2):125-130. doi: 10.1016/j.jcct.2017.11.014. Epub 2017 Dec 5.
PubMed 29217341Tummala R, Han D, Friedman J, Hayes S, Thomson L, Gransar H, Slomka P, Rozanski A, Dey D, Berman D. Association between plaque localization in proximal coronary segments and MACE outcomes in patients with mild CAC: Results from the EISNER study. Am J Prev Cardiol. 2022 Sep 27;12:100423. doi: 10.1016/j.ajpc.2022.100423. eCollection 2022 Dec.
PubMed 36199447Symons R, Morris JZ, Wu CO, Pourmorteza A, Ahlman MA, Lima JA, Chen MY, Mallek M, Sandfort V, Bluemke DA. Coronary CT Angiography: Variability of CT Scanners and Readers in Measurement of Plaque Volume. Radiology. 2016 Dec;281(3):737-748. doi: 10.1148/radiol.2016161670. Epub 2016 Sep 16.
PubMed 27636027Goeller M, Achenbach S, Herrmann N, Bittner DO, Kilian T, Dey D, Raaz-Schrauder D, Marwan M. Pericoronary adipose tissue CT attenuation and its association with serum levels of atherosclerosis-relevant inflammatory mediators, coronary calcification and major adverse cardiac events. J Cardiovasc Comput Tomogr. 2021 Sep-Oct;15(5):449-454. doi: 10.1016/j.jcct.2021.03.005. Epub 2021 Apr 3.
PubMed 33867303Rampidis G, Rafailidis V, Kouskouras K, Davidhi A, Papachristodoulou A, Samaras A, Giannakoulas G, Ziakas A, Prassopoulos P, Karvounis H. Relationship between Coronary Arterial Geometry and the Presence and Extend of Atherosclerotic Plaque Burden: A Review Discussing Methodology and Findings in the Era of Cardiac Computed Tomography Angiography. Diagnostics (Basel). 2022 Sep 9;12(9):2178. doi: 10.3390/diagnostics12092178.
PubMed 36140578Fotaki A, Puyol-Anton E, Chiribiri A, Botnar R, Pushparajah K, Prieto C. Artificial Intelligence in Cardiac MRI: Is Clinical Adoption Forthcoming? Front Cardiovasc Med. 2022 Jan 10;8:818765. doi: 10.3389/fcvm.2021.818765. eCollection 2021.
PubMed 35083303Han D, Lin A, Kuronuma K, Tzolos E, Kwan AC, Klein E, Andreini D, Bax JJ, Cademartiri F, Chinnaiyan K, Chow BJW, Conte E, Cury RC, Feuchtner G, Hadamitzky M, Kim YJ, Leipsic JA, Maffei E, Marques H, Plank F, Pontone G, Villines TC, Al-Mallah MH, de Araujo Goncalves P, Danad I, Gransar H, Lu Y, Lee JH, Lee SE, Baskaran L, Al'Aref SJ, Yoon YE, Van Rosendael A, Budoff MJ, Samady H, Stone PH, Virmani R, Achenbach S, Narula J, Chang HJ, Min JK, Lin FY, Shaw LJ, Slomka PJ, Dey D, Berman DS. Association of Plaque Location and Vessel Geometry Determined by Coronary Computed Tomographic Angiography With Future Acute Coronary Syndrome-Causing Culprit Lesions. JAMA Cardiol. 2022 Mar 1;7(3):309-319. doi: 10.1001/jamacardio.2021.5705.
PubMed 35080587Riley RD, Snell KI, Ensor J, Burke DL, Harrell FE Jr, Moons KG, Collins GS. Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes. Stat Med. 2019 Mar 30;38(7):1276-1296. doi: 10.1002/sim.7992. Epub 2018 Oct 24.
PubMed 30357870Hadamitzky M, Distler R, Meyer T, Hein F, Kastrati A, Martinoff S, Schomig A, Hausleiter J. Prognostic value of coronary computed tomographic angiography in comparison with calcium scoring and clinical risk scores. Circ Cardiovasc Imaging. 2011 Jan;4(1):16-23. doi: 10.1161/CIRCIMAGING.110.955351. Epub 2010 Sep 30.
PubMed 20884832
Study at a glance
From the registry record, last updated by the sponsor on 15 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence for Preventing Heart Disease: Observational, Single Center, Prospective and Retrospective Study
- ClinicalTrials.gov ID
- NCT06029387
- Acronym / sponsor ID
- AiPHD / AiPHD F/310003/01/X56
- Conditions
- Coronary Artery Disease
- Keywords
- Coronary Computed Tomography Angiography, Major adverse cardiovascular events, Artificial Intelligence, Proportional Hazards
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 3,000 participants (actual)
- Lead sponsor
- IRCCS San RaffaeleUniversity, hospital or other
- Collaborators
- DGS, EBIT, Dyrecta Lab, PORINI
- Study start
- 31 Jul 2023 (actual)
- Primary completion
- 14 Nov 2024 (actual)
- Study completion
- 14 Nov 2024 (actual)
- First posted
- 8 Sep 2023 (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.
Coronary artery disease (CAD) is a leading cause of mortality in western countries. Coronary computed tomography angiography (cCTA) is the first-line imaging test in patients with suspected obstructive CAD. However, in most patients, cCTA shows non-obstructive CAD. The management of patients with non-obstructive CAD is unclear. This is due to the lack of cCTA-based methods capable to assess the risk of disease progression towards developing major adverse cardiovascular events (MACEs) based on the atherosclerosis characteristics of each patient. A solution for prognostication in these patients is particularly appealing since it could allow to identify patients who can benefit of a more aggressive medical treatment and management, thus improving outcome.
Proposed methods, which include qualitative evaluations such as the identification of adverse atherosclerotic plaque characteristics or quantitative evaluations such as the quantification of atherosclerotic plaque burden, may in some cases suffer of limited reproducibility between operators and software. Most importantly, each single biomarker is insufficient to accurately predict patient risk, hence potential synergic integration of cCTA and clinical biomarkers is the key to efficiently guide the personalization of patient's management. Furthermore, the few risk stratification methods that have been proposed are not designed to work on platforms capable of deploying the solution to other clinical settings, promoting prospective or external validation
Detailed description
Background and Rationale
In recent years, cCTA has become a crucial diagnostic tool for suspected CAD, recommended as the first-line test for patients with an intermediate pre-test probability of CAD by the European Society of Cardiology (ESC) guidelines and the American HeartAssociation (AHA) and Italian guidelines , because of the well-known high negative predictive value of cCTA in ruling out obstructive CAD. However, most patients have non-obstructive CAD.
While the management of patients with obstructive CAD is established, as it revolves around further diagnostic test for ischemia evaluation or upfront coronary artery revascularization, this is not the case for patients with non-obstructive CAD. However, these cohort of patients still has a significant risk of developing major adverse cardiovascular events (MACEs) that could be prevented by implementing adequate medical therapy.
To date, many approaches have been proposed to tackle this issue. However, these proposed solutions lack the ability to provide quantitative and reproducible results with a sufficiently strong predictive value, are often proposed as a stand-alone solution without the integration with multiple prognosticator imaging and clinical parameters, and are not delivered through platforms capable of providing external validation and easy integration in the clinical workflow. Among the proposed prognostic approaches, some are based on the qualitative evaluation of coronary artery plaque features, such as positive remodeling, low attenuation of the plaque, presence of spotty calcification, and "napkin ring" sign , which is subject to significant inter-reader variability.
Other approaches rely on quantitative methods for evaluating atherosclerotic burden based on the extent of coronary artery plaques and their characteristics, such as calcium density, number of lesions, regional distribution, plaque volume, non-calcified plaque volume etc.
However, these approaches may be hampered by low reproducibility, especially among different scanner vendors. Interestingly, a new research has also shown that, besides coronary artery vessel wall characteristics, pericoronary adipose tissue attenuation carries significant predictive value, as it reflects the state of coronary inflammation that plays a key role in the development and progression of coronary atherosclerosis.
All these CAD characteristics are often analyzed independently from one to another, reducing their potential synergistic prognostic value and creating redundant variables that have negligible effect on prognosis. We propose an AI-based analysis that can integrate all this data in order to select the most important determinant of CAD progression and to discard futile features, thus creating an agile and clinically valuable risk stratification model.Furthermore, we plan to create a novel imaging marker of CAD with unfavorable outcome, to be integrated in the AI-based model, which will be based on topological features of the coronary artery tree. In fact, data on the association between coronary artery topology (e.g., vessel-length, coronary artery volume index, cross-sectional area, curvature, and tortuositv) and prognosis is scarce. However, it is known that vessel tortuosity influences wall shear stress and leads to disruption of laminar flow, resulting in endothelial dysfunction and flow alterations that may lead to atherosclerosis, eventually causing adverse cardiac events . Thus, this novel biomarker may carry a significant prognostic role. Based on these premises, our research aims to develop a novel clinical-imaging AI-based model to identify and categorize patients at high risk of disease progression and provide a more personalized management approach to improve patient outcomes.
However, besides the primary objective of creating an AI-based model for CAD risk stratification, we aim to overcome some issues that currently hamper the widespread clinical application of AI in cardiovascular care. In fact, it is recognized that the integration of AI-based applications into the clinical workflow, which will increase usability and decrease costs, is currently lacking.
We aim to tackle these issues with the help of the industrial partners involved in this project that will build a platform capable of delivering the software solution to provide external validation of the algorithm.
This platform will be characterized by state-of-the-art security measures, interoperability with current clinical software, and easy-to-use interface.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Retrospective cohort | – | –The retrospective cohort will include 2500 patients who underwent a clinically indicated cCTA examination for CAD evaluation. Duration of enrollment: enrollment of patients via a telephone call will last 15 months starting from the beginning of the study (month 0). Duration of total follow-up: no follow-up is planned. Duration of total study period: total retrospective study duration will be 30 months |
| Prospective cohort | – | –The prospective cohort will include 500 patients undergoing a clinically indicated cCTA for CAD evaluation. Duration of enrollment: enrollment of patients at the time of cCTA examination will last 12 months. Duration of total follow-up: each patient will be followed up for 36 months from the date of the cCTA. Duration of total study period: total prospective study duration will be 54 months (last patient enrolled at month 12 + 36 months of follow-up + 6 months for data analysis). |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients undergoing clinically indicated cCTA for suspected CAD
Key inclusion criteria (1 listed)
- Patients with cCTA performed for CAD assessment
Key exclusion criteria (3 listed)
- Refusal to participate in the study
- Age <18 years old
- Presence of other cardiovascular comorbidities (e.g. severe valvulopathies; non-ischemic cardiomyopathies; etc.)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
composite outcome 1
Time frame: 36 months from CCTA examination
death by cardiovascular events or non-fatal myocardial infarction
1 secondary outcome
composite outcome 2
Time frame: 36 months from CCTA examination
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Jan 2026.
- Background: PubMed 31504439Knuuti J, Wijns W, Saraste A, Capodanno D, Barbato E, Funck-Brentano C, Prescott E, Storey RF, Deaton C, Cuisset T, Agewall S, Dickstein K, Edvardsen T, Escaned J, Gersh BJ, Svitil P, Gilard M, Hasdai D, Hatala R, Mahfoud F, Masip J, Muneretto C, Valgimigli M, Achenbach S, Bax JJ; ESC Scientific Document Group. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J. 2020 Jan 14;41(3):407-477. doi: 10.1093/eurheartj/ehz425. No abstract available.
- Background: PubMed 34756653Writing Committee Members; Gulati M, Levy PD, Mukherjee D, Amsterdam E, Bhatt DL, Birtcher KK, Blankstein R, Boyd J, Bullock-Palmer RP, Conejo T, Diercks DB, Gentile F, Greenwood JP, Hess EP, Hollenberg SM, Jaber WA, Jneid H, Joglar JA, Morrow DA, O'Connor RE, Ross MA, Shaw LJ. 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol. 2021 Nov 30;78(22):e187-e285. doi: 10.1016/j.jacc.2021.07.053. Epub 2021 Oct 28.
- Background: PubMed 34160775Esposito A, Francone M, Andreini D, Buffa V, Cademartiri F, Carbone I, Clemente A, Guaricci AI, Guglielmo M, Indolfi C, La Grutta L, Ligabue G, Liguori C, Mercuro G, Mushtaq S, Neglia D, Palmisano A, Sciagra R, Seitun S, Vignale D, Pontone G, Carrabba N. SIRM-SIC appropriateness criteria for the use of Cardiac Computed Tomography. Part 1: Congenital heart diseases, primary prevention, risk assessment before surgery, suspected CAD in symptomatic patients, plaque and epicardial adipose tissue characterization, and functional assessment of stenosis. Radiol Med. 2021 Sep;126(9):1236-1248. doi: 10.1007/s11547-021-01378-0. Epub 2021 Jun 23.
- Background: PubMed 27679600Conte E, Annoni A, Pontone G, Mushtaq S, Guglielmo M, Baggiano A, Volpato V, Agalbato C, Bonomi A, Veglia F, Formenti A, Fiorentini C, Bartorelli AL, Pepi M, Andreini D. Evaluation of coronary plaque characteristics with coronary computed tomography angiography in patients with non-obstructive coronary artery disease: a long-term follow-up study. Eur Heart J Cardiovasc Imaging. 2017 Oct 1;18(10):1170-1178. doi: 10.1093/ehjci/jew200.
- Background: PubMed 23498679Otsuka K, Fukuda S, Tanaka A, Nakanishi K, Taguchi H, Yoshikawa J, Shimada K, Yoshiyama M. Napkin-ring sign on coronary CT angiography for the prediction of acute coronary syndrome. JACC Cardiovasc Imaging. 2013 Apr;6(4):448-57. doi: 10.1016/j.jcmg.2012.09.016. Epub 2013 Mar 14.
- Background: PubMed 29305348Nerlekar N, Ha FJ, Cheshire C, Rashid H, Cameron JD, Wong DT, Seneviratne S, Brown AJ. Computed Tomographic Coronary Angiography-Derived Plaque Characteristics Predict Major Adverse Cardiovascular Events: A Systematic Review and Meta-Analysis. Circ Cardiovasc Imaging. 2018 Jan;11(1):e006973. doi: 10.1161/CIRCIMAGING.117.006973.
- Background: PubMed 29217341Maroules CD, Hamilton-Craig C, Branch K, Lee J, Cury RC, Maurovich-Horvat P, Rubinshtein R, Thomas D, Williams M, Guo Y, Cury RC. Coronary artery disease reporting and data system (CAD-RADSTM): Inter-observer agreement for assessment categories and modifiers. J Cardiovasc Comput Tomogr. 2018 Mar-Apr;12(2):125-130. doi: 10.1016/j.jcct.2017.11.014. Epub 2017 Dec 5.
- Background: PubMed 36199447Tummala R, Han D, Friedman J, Hayes S, Thomson L, Gransar H, Slomka P, Rozanski A, Dey D, Berman D. Association between plaque localization in proximal coronary segments and MACE outcomes in patients with mild CAC: Results from the EISNER study. Am J Prev Cardiol. 2022 Sep 27;12:100423. doi: 10.1016/j.ajpc.2022.100423. eCollection 2022 Dec.
- Background: PubMed 27636027Symons R, Morris JZ, Wu CO, Pourmorteza A, Ahlman MA, Lima JA, Chen MY, Mallek M, Sandfort V, Bluemke DA. Coronary CT Angiography: Variability of CT Scanners and Readers in Measurement of Plaque Volume. Radiology. 2016 Dec;281(3):737-748. doi: 10.1148/radiol.2016161670. Epub 2016 Sep 16.
- Background: PubMed 33867303Goeller M, Achenbach S, Herrmann N, Bittner DO, Kilian T, Dey D, Raaz-Schrauder D, Marwan M. Pericoronary adipose tissue CT attenuation and its association with serum levels of atherosclerosis-relevant inflammatory mediators, coronary calcification and major adverse cardiac events. J Cardiovasc Comput Tomogr. 2021 Sep-Oct;15(5):449-454. doi: 10.1016/j.jcct.2021.03.005. Epub 2021 Apr 3.
- Background: PubMed 36140578Rampidis G, Rafailidis V, Kouskouras K, Davidhi A, Papachristodoulou A, Samaras A, Giannakoulas G, Ziakas A, Prassopoulos P, Karvounis H. Relationship between Coronary Arterial Geometry and the Presence and Extend of Atherosclerotic Plaque Burden: A Review Discussing Methodology and Findings in the Era of Cardiac Computed Tomography Angiography. Diagnostics (Basel). 2022 Sep 9;12(9):2178. doi: 10.3390/diagnostics12092178.
- Background: PubMed 35083303Fotaki A, Puyol-Anton E, Chiribiri A, Botnar R, Pushparajah K, Prieto C. Artificial Intelligence in Cardiac MRI: Is Clinical Adoption Forthcoming? Front Cardiovasc Med. 2022 Jan 10;8:818765. doi: 10.3389/fcvm.2021.818765. eCollection 2021.
- Individual participant dataSharing planned: No
Related trials for Coronary Artery Disease
RecruitingPhase 4
Efficacy and Safety of a Half-dose Bolus of r-SAK Prior to Primary PCI in ST-elevation Myocardial Infarction
ST Elevation Myocardial Infarction
Active, not recruitingPhase 4
Women's IschemiA TRial to Reduce Events In Non-ObstRuctive CAD
Coronary Artery Disease
RecruitingPhase 4
CCTA Evaluation of SGLT2i-related Pericoronary Fat Changes Patients With Stage 4 CKM Syndrome
ACS (Acute Coronary Syndrome), SGLT2 Inhibitors
Not yet recruiting
Effect of Propionic Acid Supplementation on Endothelial Function
Cardiovascular Diseases, Endothelial Dysfunction
Recruiting
Intracoronary Cryotherapy Effect on Stabilization of Vulnerable Plaque in Patients With Stable Angina or ACS - A Traditional Feasibility Study
Coronary Arterial Disease (CAD), Plaque
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.
Antonio Esposito principal investigator
IRCCS San Raffaele
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, run by IRCCS San Raffaele at 1 site in Italy.
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 1 inclusion and 3 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: finished, as of the sponsor's last update on 15 Jan 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 Italy. 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 is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
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?
composite outcome 1 (time frame: 36 months from CCTA examination). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06029387, which also lists 17 linked publications.
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 15 Jan 2026.