Heart Failure Clinical Trial
— CADHOOfficial title:
Grenoble Cardiovascular Digital Health Data Observatory
NCT number | NCT05316025 |
Other study ID # | 38RC21.197 |
Secondary ID | |
Status | Not yet recruiting |
Phase | |
First received | |
Last updated | |
Start date | May 2022 |
Est. completion date | January 2025 |
The COVID-19 health crisis has led to a drastic decrease in the rate of myocardial infarction without the causes being completely identified. They are probably multiple, but this crisis has confirmed the need for massive health data from different horizons to better assess coronary disease in order to develop precision medicine. This objective is now achievable thanks to the use of tools such as big data and artificial intelligence (AI). Our team is developing algorithms to analyze medical images and identify people at risk of major cardiovascular events. These algorithms which are developed with retrospective data must be validated on prospective data, which is the objective of the Grenoble cardiovascular digital health data observatory. The algorithm that will be validated is currently being created as part of a RIPH 3 study "AIDECORO" (NCT: 04598997). It is being developed from clinical, biological and imaging data from 600 patients with ST+ infarction and 1000 "control" patients who have undergone coronary angiography (these data are exported and stored in the PREDIMED health data warehouse via the hospital information system).
Status | Not yet recruiting |
Enrollment | 5000 |
Est. completion date | January 2025 |
Est. primary completion date | January 2025 |
Accepts healthy volunteers | |
Gender | All |
Age group | 18 Years and older |
Eligibility | Inclusion Criteria: - Adult patients who have undergone coronary angiography at CHUGA for whom images are usable. - No opposition to participation Exclusion Criteria: - Coronary image not usable - Persons referred to in articles L1121-5 to L-1121-8 of the CSP - Patients living outside the Rhône Alpes region. |
Country | Name | City | State |
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n/a |
Lead Sponsor | Collaborator |
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University Hospital, Grenoble |
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Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Prospectively validate cardiovascular medical image analysis algorithms capable of identifying patients with poor prognostic criteria using artificial intelligence and big data methods. | The rate of occurrence of death or hospitalization for heart failure during follow-up. | Through study completion, an average of 1 year | |
Secondary | Evaluate the predictive performance of algorithms to identify patients with persistent anginal symptoms. | Seattle Angina Questionnaire summary score to 12 months | 12 months | |
Secondary | Evaluate the predictive performance of algorithms to identify patients with persistent dyspnea symptoms. | Rose Angina Questionnaire to 12 months | 12 months | |
Secondary | Evaluate the predictive performance of algorithms to identify patients with good disease perception. | Seattle Angina Questionnaire to 12 months | 12 months | |
Secondary | Evaluate the predictive performance of algorithms to identify patients satisfied with their care. | Seattle Angina Questionnaire to 12 months | 12 months | |
Secondary | Evaluate the predictive performance of the algorithms for quality of life at one year. | EuroQOL (EQ-5D-5L) to 12 months | 12 months | |
Secondary | Evaluate the predictive performance of algorithms for healthcare consumption | Average annual cost of care to 12 months | 12 months | |
Secondary | Assessing the prognostic value of frailty in coronary artery disease | Dynanometry | Day one | |
Secondary | Assessing the prognostic value of environmental influence in coronary artery disease | Measurement of air pollutants from the SIRANE dispersion model | Day one |
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