Cardiovascular Digital Health Data Observatory

Not yet recruiting

• Conditions: Observatory; Cardiovascular Diseases; Coronarography; Health Data; Cardiovascular; Digital; Heart Failure

• Registration Number: NCT05316025
• Lead Sponsor: University Hospital, Grenoble

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.

Study Timeline

• Status Verified: 2022-01
• Study First Submitted: 2022-02-03
• Study First Submitted QC: 2022-03-29
• Last Update Submitted: 2022-03-29
• Study First Posted: 2022-04-07
• Last Update Posted: 2022-04-07
• Study Start: 2022-05
• Primary Completion: 2025-01
• Study Completion: 2025-01

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 5000

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Prospectively validate cardiovascular medical image analysis algorithms capable of identifying patients with poor prognostic criteria using artificial intelligence and big data methods.	Through study completion, an average of 1 year	The rate of occurrence of death or hospitalization for heart failure during follow-up.

Secondary Outcome Measures

Name	Time	Method
Evaluate the predictive performance of algorithms to identify patients with persistent anginal symptoms.	12 months	Seattle Angina Questionnaire summary score to 12 months
Evaluate the predictive performance of algorithms to identify patients with persistent dyspnea symptoms.	12 months	Rose Angina Questionnaire to 12 months
Assessing the prognostic value of frailty in coronary artery disease	Day one	Dynanometry
Evaluate the predictive performance of algorithms for healthcare consumption	12 months	Average annual cost of care to 12 months
Assessing the prognostic value of environmental influence in coronary artery disease	Day one	Measurement of air pollutants from the SIRANE dispersion model
Evaluate the predictive performance of the algorithms for quality of life at one year.	12 months	EuroQOL (EQ-5D-5L) to 12 months
Evaluate the predictive performance of algorithms to identify patients with good disease perception.	12 months	Seattle Angina Questionnaire to 12 months
Evaluate the predictive performance of algorithms to identify patients satisfied with their care.	12 months	Seattle Angina Questionnaire to 12 months