Clinical trial NCT05316025: Cardiovascular Digital Health Data Observatory
Official title: Grenoble Cardiovascular Digital Health Data Observatory
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Cardiovascular Diseases
Digital
Health Data
Observatory
Coronarography
Heart Failure
Cardiovascular
Keywords: Deep learning
Observatory
Cardiovascular
Health data
Lead sponsor: University Hospital, Grenoble
Sponsor class: OTHER
First posted: 2022-04-07
Enrollment: 5000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: All patients receiving coronary angiography during hospitalization for suspected or managed coronary artery disease at CHUGA.

Brief summary: 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).

Detailed description: This a type 3 study of the Jardé law, involving the human person, It is a study : observational study, prospective, descriptive, monocentric

The main objective of the study is to prospectively validate cardiovascular medical image analysis algorithms capable of identifying patients with poor prognostic criteria using artificial intelligence and big data methods.

The primary endpoint is the rate of occurrence of death or hospitalization for heart failure during follow-up.

The predictive accuracy of the algorithms will be assessed by calculating the sensitivity, specificity, positive predictive value, and negative predictive value on the prospective cohort.

Patients who are to undergo coronary angiography during a hospitalization in the cardiology department are prospectively recruited after obtaining their non opposition. The data were collected using the CARDIO Datamart developed by the PREDIMED health data host. The collection of the primary endpoint (death from any cause and hospitalization for heart failure) will be performed by telephone follow-up.

The number of subjects needed for this study is 5000 patients.

The prospective validation of the algorithm developed retrospectively in the AIDECORO project (coronary image) will make it possible to move towards the last stage of the project, which will consist of evaluating in a randomized study the superiority of precision medicine using this algorithm, allowing for therapeutic escalation or de-escalation according to the predictive risk evaluated by the algorithm in relation to usual management.

Eligibility criteria: 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.

Primary outcomes:
measure: Prospectively validate cardiovascular medical image analysis algorithms capable of identifying patients with poor prognostic criteria using artificial intelligence and big data methods.
timeFrame: Through study completion, an average of 1 year
Secondary outcomes:
measure: Evaluate the predictive performance of algorithms to identify patients with persistent anginal symptoms.
timeFrame: 12 months
measure: Evaluate the predictive performance of algorithms to identify patients with persistent dyspnea symptoms.
timeFrame: 12 months
measure: Evaluate the predictive performance of algorithms to identify patients with good disease perception.
timeFrame: 12 months
measure: Evaluate the predictive performance of algorithms to identify patients satisfied with their care.
timeFrame: 12 months
measure: Evaluate the predictive performance of the algorithms for quality of life at one year.
timeFrame: 12 months
measure: Evaluate the predictive performance of algorithms for healthcare consumption
timeFrame: 12 months
measure: Assessing the prognostic value of frailty in coronary artery disease
timeFrame: Day one
measure: Assessing the prognostic value of environmental influence in coronary artery disease
timeFrame: Day one
Other outcomes:
Locations:
Study officials:
affiliation: CHU Grenoble Alpes
name: Gilles Barone-Rochette
role: PRINCIPAL_INVESTIGATOR
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