Automatic Anatomical and Functional Classification of Coronary Arteries in CT Scans Using Artificial Intelligence.
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 1,670
- 主要终点
- Predictive performance, at the coronary vessel level, of an intelligent Coronary CT AI based image analysis system on the detection of a stenosis requiring intervention, versus invasive coronary angiography with reference measurement (FFR).
研究概览
简要总结
The goal of this Category 3 research involving the human person is to predict the measurement of the post-stenosis flow (FFR) using CTTA coupled with an intelligent predictive analysis system and comparing it with invasive coronary angiography FFR as measurement of reference.
The population studied are adult patients,- with no diagnosed coronary status or history of stenting or bypass surgery- with indication for FFR measurement.
The main question it aims to answer is:
• Can, in a single acquisition, CTTA coupled with AI produce good predictive performance of stenosis and FFR ? If it can it will allow us to avoid the need for invasive FFR.
For patients who will be included in the retrospective part: only their data from their medical records will be used.
Patients who will be included in the prospective part will additionally complete the EQ5D5L questionnaire before coronary angiography and at the end of the patient's participation (4 months after the CCTA).
There is a no comparison group, the predictive FFR from CTTA of a patient will be compared with angiography FFR from the same patient, same vessel.
详细描述
Coronary disease is the leading cause of death in the world with 18 million deaths according to the WHO The exploration of chest pain suggesting coronary artery disease now gives preference to coronary CT angiography (CCTA) for its high sensitivity (95%) in the non-invasive detection of patients with coronary artery disease.
This is a class I recommendation with a high level of evidence according to the AHA/ACC recommendations of November 2021. The interpretation of the images aims to define the degree of stenosis of the vessels, a stenosis >= 50% being likely to limit coronary flow. However, the degree of stenosis, especially between 40%-90%, is not directly correlated with its functional impact. This must therefore be assessed by an invasive intra-arterial examination during coronary angiography. The measurement of the post-stenosis flow (FFR) may indicate a stent or bypass operation when the FFR is less than 0.8.
260,000 coronary angiographies are performed each year in France, of which two-thirds do not lead to an interventional procedure. CCTA coupled with an intelligent predictive analysis system could reduce this rate of invasive examinations that do not require an interventional procedure. Various decision support systems have been developed recently using AI methods, either for stenosis assessment or for FFR estimation. Their overall sensitivity is insufficient, mainly because their training base is small (<100 cases) and they have not been validated in a multicenter setting. On the contrary, the radiology department of the IMM has built a large base of images (n=5000) of CCTA from various machines, qualified by an expert and associated with FFR values. This learning base feeding a deep learning system has very good predictive performances of stenosis and FFR on a new test base of CCTA images alone. Obtaining these 2 parameters in a single acquisition would enhance the radiologist's accuracy and avoid the need for invasive FFR. It therefore seems appropriate to reinforce this system with a multicenter feed and to perform an external validation on an independent sample.
OBJECTIVES Main : Predictive performance, at the coronary vessel level, of an intelligent Coronary CT AI based image analysis system on the detection of a stenosis requiring intervention, versus invasive coronary angiography with reference measurement (FFR).
Secondary:- predictive performance regarding the indication for intervention at the patient level (i.e., the synthesis of all the assessments of his or her vessels) - medico-economic analysis of the cost-effectiveness type comparing two diagnostic strategies (CCTA+AI, vs. usual care = CCTA + invasive FFR) in terms of effectiveness (shortening of the time to obtain treatment, unnecessary invasive coronary angiography avoided, complications avoided), cost and incremental cost-effectiveness ratio
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Other
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 patient,
- •with no diagnosed coronary status or history of stenting or bypass surgery
- •whose CCTA evaluation by the local radiologist results in at least an intermediate stenosis ≥40% on at least one vessel with indication for FFR measurement.
- •Who has not expressed opposition to the use of their data.
排除标准
- •protected populations: patient under guardianship, curatorship or legal protection.
结局指标
主要结局
Predictive performance, at the coronary vessel level, of an intelligent Coronary CT AI based image analysis system on the detection of a stenosis requiring intervention, versus invasive coronary angiography with reference measurement (FFR).
时间窗: 2 years
This criterion is calculated on the validation sample. Sensitivity at the vessel level will be calculated as the ratio of the number of stenotic vessels classified as interventional by the AI system to the total number of stenotic vessels classified as interventional by the reference method. The other metrics (specificity, likelihood ratios, prevalence and predictive errors) will be calculated, all with their 95% confidence intervals.
次要结局
- Predictive performance regarding the indication for intervention at the patient level and medico-economic analysis of the cost-effectiveness type comparing two diagnostic strategies strategies (CCTA+AI, vs. usual care = CCTA + invasive FFR)(2 years)