Grenoble Cardiovascular Digital Health Data Observatory
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 5,000
- 主要终点
- Prospectively validate cardiovascular medical image analysis algorithms capable of identifying patients with poor prognostic criteria using artificial intelligence and big data methods.
研究概览
简要总结
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).
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Adult patients who have undergone coronary angiography at CHUGA for whom images are usable.
- •No opposition to participation
排除标准
- •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.
结局指标
主要结局
Prospectively validate cardiovascular medical image analysis algorithms capable of identifying patients with poor prognostic criteria using artificial intelligence and big data methods.
时间窗: Through study completion, an average of 1 year
The rate of occurrence of death or hospitalization for heart failure during follow-up.
次要结局
- Evaluate the predictive performance of algorithms to identify patients with persistent anginal symptoms.(12 months)
- Evaluate the predictive performance of algorithms to identify patients with persistent dyspnea symptoms.(12 months)
- Assessing the prognostic value of frailty in coronary artery disease(Day one)
- Evaluate the predictive performance of algorithms for healthcare consumption(12 months)
- Assessing the prognostic value of environmental influence in coronary artery disease(Day one)
- Evaluate the predictive performance of the algorithms for quality of life at one year.(12 months)
- Evaluate the predictive performance of algorithms to identify patients with good disease perception.(12 months)
- Evaluate the predictive performance of algorithms to identify patients satisfied with their care.(12 months)