Artificial Intelligence With DEep Learning on COROnary Microvascular Disease

NCT04598997 | Unknown DMC

• 2 other identifiers: 38RC20.3072020-A02379-30
• Study Type: observational
• Target: 600
• Locations: 1 country
• Sites: 1

Brief Summary

Despite the progress made in the management of myocardial infarction (MI), the associated morbidity and mortality remains high. Numerous scientific data show that damage of the coronary microcirculation (CM) during a STEMI remains a problem because the techniques for measuring it are still imperfect. We have simple methods for estimating the damage to the MC during the initial coronary angiography, the best known being the calculation of the myocardial blush grade (MBG), but which is semi-quantitative and therefore not very precise, or more precise imaging techniques, such as cardiac MRI, which are performed 48 hours after the infarction and which make the development of early applicable therapeutics not very propitious. Finally, lately, the use of special coronary guides to measure a precise CM index remains non-optimal because it prolongs the procedure. However, the information is in the picture and this information could allow the development of therapeutic strategies adapted to the patient's CM. Indeed, the arrival of iodine in CM increases the density of the pixels of the image, this has been demonstrated by the implementation in 2009 of a software allowing the calculation of the MBG assisted by computer. But the performances of this software did not allow its wide diffusion. Today, the field of medical image analysis presents dazzling progress thanks to artificial intelligence (AI). Deep Learning, a sub-category of Machine Learning, is probably the most powerful form of AI for automated image analysis today. Made up of a network of artificial neurons, it allows, using a very large number of known examples, to extract the most relevant characteristics of the image to solve a given problem. Thus, it uses thousands of pieces of information, sometimes imperceptible to the naked eye. We hypothesize that a supervised Deep Learning algorithm trained with a set of relevant data, will be able to identify a patient with a pejorative prognosis, probably related to a microcirculatory impairment visible in the image.

Conditions

Coronary Microvascular Disease; Artificial Intelligence; Cardiovascular Diseases; Heart Failure

Keywords

Artificial intelligence ; Coronary Microvascular Disease

Outcome Measures

Primary Outcomes (1)

• Death or re-hospitalization for heart Failure

  The predictive accuracy will be evaluated by calculating the sensitivity, specificity, positive predictive value, and negative predictive value on the test cohort.

  Baseline (at the time of the phone call) - From nov 2020 and jan 2021 [anticipated]

Secondary Outcomes (1)

• Algorithm study

  After data annotation (step 2) and developping the algorithm (step 3) - In Jan 2022 [anticipated]

Study Arms (2)

600 patients involved in the prospective study

These patients will be contacted by telephone follow-up, offered participation in the study and sent the information and non-opposition letter. In case of refusal, data will not be used.

1000 patients involved in a non-human study

To train the algorithm to recognize images in the context of STEMI revascularization, 1000 normal coronary angiograms performed in a stable disease context will also be identified.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adult patients undergoing coronary angioplasty revascularization at CHUGA for STEMI from 2015 to 2018.

You may qualify if:

• Age over 18 years
• Patients who have undergone coronary angioplasty revascularization at CHUGA for STEMI from 2015 to 2018 for which images are usable.
• Patient affiliated with social security
• Non-opposition to participation

You may not qualify if:

• Coronary artery image not usable
• Patient under guardianship or deprived of liberty

Sponsors & Collaborators

• University Hospital, Grenoble: lead

Study Sites (1)

Chu Grenoble Alpes

Grenoble, 38043, France

RECRUITING

MeSH Terms

Conditions

Microvascular Angina; Cardiovascular Diseases; Heart Failure

Condition Hierarchy (Ancestors)

Angina Pectoris; Myocardial Ischemia; Heart Diseases; Vascular Diseases

Study Officials

• Gilles Barone-Rochette

  University Hospital, Grenoble

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Gilles Barone-Rochette

CONTACT

+33476765172; gbarone@chu-grenoble.fr

Pauline PERETOUT

CONTACT

+33476766700; pperetout@chu-grenoble.fr

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: October 5, 2020
• First Posted: October 22, 2020
• Study Start: October 20, 2020
• Primary Completion: November 1, 2024
• Study Completion: November 1, 2024
• Last Updated: June 24, 2024

Record last verified: 2023-12

Data Sharing

• IPD Sharing: Will not share

Locations

FR (1)