Coronary Microvascular DiseaseAIDECORONCT04598997
Artificial Intelligence With DEep Learning on COROnary Microvascular Disease
An observational study of Coronary Microvascular Disease and Artificial Intelligence, run by University Hospital, Grenoble at 1 site in France.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Coronary Microvascular Disease, Artificial Intelligence, Cardiovascular Diseases
- How many people
- 600 participants
- Where
- 1 siteFrance
- Study dates
- 20 Oct 2020to 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: Adult patients undergoing coronary angioplasty revascularization at CHUGA for STEMI from 2015 to 2018.
Who may be able to join
- 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
Who cannot join
- Coronary artery image not usable
- Patient under guardianship or deprived of liberty
Trial locations and contacts
1 site in 1 country.
France1 site, 1 recruiting
- Recruiting
Chu Grenoble Alpes
Grenoble, 38043
Gilles Barone-Rochette, +33476765172, gbarone@chu-grenoble.fr
About this study
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.
Read the detailed description
The aim of this study is to develop an algorithm capable of identifying patients with poor prognosis criteria at the time of hospitalization for STEMI, despite successful revascularization by analyzing coronary angiography images using supervised Deep Learning type artificial intelligence methods.
The protocol will be subdivided into 4 steps:
- Step 1: Patient selection
Data mining to identify and select patients via PMSI data. Patients will be contacted by telephone follow-up to check the participation agreement and collect the primary outcome. Other data from the patient's medical file will be collected through PREDIMED.
- Step 2: Data annotation
To identify for each patient with successful revascularization according to the usual criteria (TIMI Flow = 3, MBG = 2 or 3 and ST segment resolution > 70%) whether or not he or she presents, at the time of hospitalization for STEMI, pejorative evolution criteria defined by the occurrence of death or rehospitalization for heart Failure at the time of follow-up . This step requires the expertise of an angioplastician and will result in the generation of a database of 600 cases. To train the algorithm to recognize images in the context of STEMI revascularization, 1000 normal coronary angiographies performed in a stable disease context will also be identified.
- Step 3: Development of a new method for analyzing coronary angiography images to identify patients with non-optimal revascularization.
Develop using Tensorflow/Keras libraries a supervised Deep Learning AI algorithm trained to identify patients with non-optimal revascularization (patient with poor prognosis). The algorithm will be based on convolutional neural network methodology and the model will be trained using data from the two previous steps. All or part of the sequence of interest will be used at the input of the model which will propose at the output a probability of good or bad prognosis of the patient.The 1000 complementary coronary angiographies will be used to artificially increase the learning base by increasing the number of cases or will be exploited for a transfer learning method.
- Step 4: Evaluation of the pathophysiological hypothesis.
The main weakness of AI is the "Black Box". That is, the algorithm can predict correctly without knowing how. It is then difficult to link the result to a physiopathological phenomenon and to develop therapeutics. Here we will evaluate the correlation of the algorithm's result with the reference method for measuring CD used in the patients of the Guardiancory study (NCT03087175).
- Conditions
- Coronary Microvascular Disease, Artificial Intelligence, Cardiovascular Diseases, Heart Failure
- Keywords
- Artificial intelligence ; Coronary Microvascular Disease
- Medical subject headings
- Microvascular Angina, Cardiovascular Diseases, Heart Failure
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 |
|---|---|---|
| 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Baseline (at the time of the phone call) - From nov 2020 and jan 2021 [anticipated]
Secondary outcomes (1)
Algorithm study
Study of the correlations of the result of the algorithm with the reference method for measuring coronary microcirculation.
Measured: After data annotation (step 2) and developping the algorithm (step 3) - In Jan 2022 [anticipated]
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Oct 2020
- Study start20 Oct 2020
- Primary completionNov 2024 (estimated)
- Study completionNov 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Coronary Microvascular 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
- 600 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University Hospital, Grenoble
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Gilles Barone-Rochette, Principal investigator, University Hospital, Grenoble
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04598997
- Sponsor's protocol ID
- 38RC20.307
- Other IDs
- 2020-A02379-30
- Acronym
- AIDECORO
- Status last verified
- Dec 2023
- First submitted
- 5 Oct 2020
- Last updated
- 24 Jun 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 24 Jun 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence With DEep Learning on COROnary Microvascular Disease
- ClinicalTrials.gov ID
- NCT04598997
- Acronym / sponsor ID
- AIDECORO / 38RC20.307
- Conditions
- Coronary Microvascular Disease, Artificial Intelligence, Cardiovascular Diseases, Heart Failure
- Keywords
- Artificial intelligence ; Coronary Microvascular Disease
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 600 participants (estimated)
- Lead sponsor
- University Hospital, GrenobleUniversity, hospital or other
- Study start
- 20 Oct 2020 (actual)
- Primary completion
- Nov 2024 (estimated)
- Study completion
- Nov 2024 (estimated)
- First posted
- 22 Oct 2020 (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.
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.
Detailed description
The aim of this study is to develop an algorithm capable of identifying patients with poor prognosis criteria at the time of hospitalization for STEMI, despite successful revascularization by analyzing coronary angiography images using supervised Deep Learning type artificial intelligence methods.
The protocol will be subdivided into 4 steps:
- Step 1: Patient selection
Data mining to identify and select patients via PMSI data. Patients will be contacted by telephone follow-up to check the participation agreement and collect the primary outcome. Other data from the patient's medical file will be collected through PREDIMED.
- Step 2: Data annotation
To identify for each patient with successful revascularization according to the usual criteria (TIMI Flow = 3, MBG = 2 or 3 and ST segment resolution > 70%) whether or not he or she presents, at the time of hospitalization for STEMI, pejorative evolution criteria defined by the occurrence of death or rehospitalization for heart Failure at the time of follow-up . This step requires the expertise of an angioplastician and will result in the generation of a database of 600 cases. To train the algorithm to recognize images in the context of STEMI revascularization, 1000 normal coronary angiographies performed in a stable disease context will also be identified.
- Step 3: Development of a new method for analyzing coronary angiography images to identify patients with non-optimal revascularization.
Develop using Tensorflow/Keras libraries a supervised Deep Learning AI algorithm trained to identify patients with non-optimal revascularization (patient with poor prognosis). The algorithm will be based on convolutional neural network methodology and the model will be trained using data from the two previous steps. All or part of the sequence of interest will be used at the input of the model which will propose at the output a probability of good or bad prognosis of the patient.The 1000 complementary coronary angiographies will be used to artificially increase the learning base by increasing the number of cases or will be exploited for a transfer learning method.
- Step 4: Evaluation of the pathophysiological hypothesis.
The main weakness of AI is the "Black Box". That is, the algorithm can predict correctly without knowing how. It is then difficult to link the result to a physiopathological phenomenon and to develop therapeutics. Here we will evaluate the correlation of the algorithm's result with the reference method for measuring CD used in the patients of the Guardiancory study (NCT03087175).
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adult patients undergoing coronary angioplasty revascularization at CHUGA for STEMI from 2015 to 2018.
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (2 listed)
- Coronary artery image not usable
- Patient under guardianship or deprived of liberty
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
Death or re-hospitalization for heart Failure
Time frame: Baseline (at the time of the phone call) - From nov 2020 and jan 2021 [anticipated]
The predictive accuracy will be evaluated by calculating the sensitivity, specificity, positive predictive value, and negative predictive value on the test cohort.
1 secondary outcome
Algorithm study
Time frame: After data annotation (step 2) and developping the algorithm (step 3) - In Jan 2022 [anticipated]
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Jun 2024.
- Individual participant dataSharing planned: No
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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.
Gilles Barone-Rochette principal investigator
University Hospital, Grenoble
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 Microvascular Disease and Artificial Intelligence, run by University Hospital, Grenoble at 1 site in France.
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 4 inclusion and 2 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: status not confirmed, as of the sponsor's last update on 24 Jun 2024. 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 France. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Gilles Barone-Rochette (+33476765172, gbarone@chu-grenoble.fr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Death or re-hospitalization for heart Failure (time frame: Baseline (at the time of the phone call) - From nov 2020 and jan 2021 [anticipated]). 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 24 Jun 2024.