Artificial Intelligence for Preventing Heart Disease (AiPHD): Observational, Single Center, Prospective and Retrospective Study

Coronary Artery Disease Clinical Trial

— AiPHD  

Official title:

Artificial Intelligence for Preventing Heart Disease: Observational, Single Center, Prospective and Retrospective Study

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06029387
• Other study ID #: AiPHD F/310003/01/X56
• Status: Recruiting
• Start date: July 31, 2023
• Est. completion date: March 1, 2028

Study information

• Verified date: April 2024
• Source: IRCCS San Raffaele
• Contact: Antonio Esposito
• Phone: 02 2643 6102
• Email: esposito.antonio[@]hsr.it
• Is FDA regulated: No
• Study type: Observational

Clinical 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. 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

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 3000
• Est. completion date: March 1, 2028
• Est. primary completion date: August 31, 2027
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Patients with cCTA performed for CAD assessment

Exclusion Criteria:

1. Refusal to participate in the study

2. Age <18 years old

3. Presence of other cardiovascular comorbidities (e.g. severe valvulopathies; non-ischemic cardiomyopathies; etc.)

Related Conditions & MeSH terms

• Coronary Artery Disease
• Heart Diseases

Locations

Country	Name	City	State
Italy	IRCCS San Raffaele	Milano

Sponsors (5)

Lead Sponsor	Collaborator
IRCCS San Raffaele	DGS, Dyrecta Lab, EBIT, PORINI

Country where clinical trial is conducted

Italy, 

References & Publications (17)

Conte 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. — View Citation

Esposito 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. — View Citation

Fotaki 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. — View Citation

Goeller 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. — View Citation

Hadamitzky 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. — View Citation

Han 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. — View Citation

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. Erratum In: Eur Heart J. 2020 Nov 21;41(44):4242. — View Citation

Maroules 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. — View Citation

Nerlekar 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. — View Citation

Otsuka 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. — View Citation

Pavlou M, Qu C, Omar RZ, Seaman SR, Steyerberg EW, White IR, Ambler G. Estimation of required sample size for external validation of risk models for binary outcomes. Stat Methods Med Res. 2021 Oct;30(10):2187-2206. doi: 10.1177/09622802211007522. Epub 2021 Apr 21. — View Citation

Rampidis 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. — View Citation

Riley 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. Erratum In: Stat Med. 2019 Dec 30;38(30):5672. — View Citation

Riley RD, Van Calster B, Collins GS. A note on estimating the Cox-Snell R2 from a reported C statistic (AUROC) to inform sample size calculations for developing a prediction model with a binary outcome. Stat Med. 2021 Feb 20;40(4):859-864. doi: 10.1002/sim.8806. Epub 2020 Dec 7. — View Citation

Symons 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. — View Citation

Tummala 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. — View Citation

Writing 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. — View Citation

* Note: There are 17 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Other	change of atherosclerotic burden	regression, stability or increase measured as absolute and relative change of atherosclerotic burden between baseline and repeated CCTA	12 months from CCTA examination
Primary	composite outcome 1	death by cardiovascular events or non-fatal myocardial infarction	36 months from CCTA examination
Secondary	composite outcome 2	all cause mortality, non-fatal myocardial infarction, hospitalization due to angina or angina-like symptoms and late coronary revascularization	36 months from CCTA examination