Developing Trustworthy Artificial Intelligence (AI)-Driven Tools to Predict Vascular Disease Risk and Progression
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 11,000
- 试验地点
- 6
- 主要终点
- Development of disease progression prediction algorithms
研究概览
简要总结
The VASCULAID-RETRO study, within the broader VASCULAID project, aims to create artificial intelligence (AI) algorithms that can predict cardiovascular events and the progression of abdominal aortic aneurysm (AAA) and peripheral arterial disease (PAD). The study plans to gather and analyze data from at least 5000 AAA and 6000 PAD patients, combining existing cohorts and retrospectively collected data. During this project, AI tools will be developed to perform automatic anatomical segmentation and analyses on multimodal imaging. AI prediction algorithms will be developed based on multisource data (imaging, medical history, -omics).
详细描述
To date, it is unknown which abdominal aortic aneurysm (AAA) and peripheral arterial disease (PAD) patients will suffer cardiovascular events or in which patients the AAA or PAD will progress. In the VASCULAID project, the VASCULAID-RETRO study aims to leverage data from existing cohorts and retrospectively collected data to develop artificial intelligence (AI) algorithms able to evaluate the risk of cardiovascular events and extent of disease progression.
In order to build and train the algorithms for the predictions, we plan to retrospectively enroll at least 5000 AAA and 6000 PAD patients AI-tools will be applied to the patient data. Automatic anatomical segmentation on images and image analysis on US, CTA and MRI will be performed. Also, algorithms to predict cardiovascular events and AAA or PAD progression based on multi-source data analysis will be developed.
Patient data from European clinical consortium partners is available. This consortium has access to big cohorts with relevant data for the envisioned study that will be used to enrich the existing registries. These data will be used to refine the algorithms developed for the prediction of cardiovascular events and AAA/PAD progression.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 40 Years 至 90 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Males and females, 40-90 years old, with an AAA >3cm. This includes patients with infrarenal, juxtarenal, suprarenal, iliac (defined as 1.5x its normal diameter) aneurysms, as well as mycotic aneurysms. Patients that have had interventions or ruptures will also be included
- •Males and females, 40-90 years old, all PAD patients (Fontaine stages 1,2,3, and 4).
排除标准
- •Patients with an ascending, thoracic, thoracoabdominal (type 1-3) aneurysm.
结局指标
主要结局
Development of disease progression prediction algorithms
时间窗: 3 years
The primary goal of this retrospective study is to develop and train algorithms to predict disease progression and risk of cardiovascular events in AAA and PAD patients by leveraging multi-parametric data from 5000 AAA (\>1000 in AUMC) and 6000 PAD (\>1000 in AUMC) patients from existing cohorts and biobanks.
次要结局
- Internal validation of disease progression prediction algorithms(3 years)
研究者
Kak Khee Yeung
M.D., Ph.D., FEBVS
Amsterdam UMC, location VUmc