- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT04598997
Artificial Intelligence With DEep Learning on COROnary Microvascular Disease (AIDECORO)
Study Overview
Status
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 Type
Enrollment (Anticipated)
Contacts and Locations
Study Contact
- Name: Gilles Barone-Rochette
- Phone Number: +33476765172
- Email: gbarone@chu-grenoble.fr
Study Contact Backup
- Name: Pauline PERETOUT
- Phone Number: +33476766700
- Email: pperetout@chu-grenoble.fr
Study Locations
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Grenoble, France, 38043
- Recruiting
- CHU Grenoble Alpes
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Contact:
- Gilles Barone-Rochette
- Phone Number: +33476765172
- Email: gbarone@chu-grenoble.fr
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-
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion Criteria:
- 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
Exclusion Criteria:
- Coronary artery image not usable
- Patient under guardianship or deprived of liberty
Study Plan
How is the study designed?
Design Details
- Observational Models: Cohort
- Time Perspectives: Prospective
Cohorts and Interventions
Group / Cohort |
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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.
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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.
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What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Death or re-hospitalization for heart Failure
Time Frame: Baseline (at the time of the phone call) - From nov 2020 and jan 2021 [anticipated]
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The predictive accuracy will be evaluated by calculating the sensitivity, specificity, positive predictive value, and negative predictive value on the test cohort.
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Baseline (at the time of the phone call) - From nov 2020 and jan 2021 [anticipated]
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Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Algorithm study
Time Frame: After data annotation (step 2) and developping the algorithm (step 3) - In Jan 2022 [anticipated]
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Study of the correlations of the result of the algorithm with the reference method for measuring coronary microcirculation.
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After data annotation (step 2) and developping the algorithm (step 3) - In Jan 2022 [anticipated]
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Collaborators and Investigators
Sponsor
Investigators
- Principal Investigator: Gilles Barone-Rochette, University Hospital, Grenoble
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Anticipated)
Study Completion (Anticipated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- 38RC20.307
- 2020-A02379-30 (Other Identifier: ID RCB)
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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