Intelligent Early Warning of Ischemic Cerebrovascular Disease Based on Multi-Source Data Fusion and Demonstration of Tiered Prevention and Control in Beijing
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 26,000
- 主要终点
- 3-Year Cumulative Incidence of First-Ever Ischemic Stroke
研究概览
简要总结
This study will evaluate whether an artificial intelligence (AI)-driven dynamic health management strategy can help prevent ischemic stroke in adults at high risk of stroke. Participants will be identified through community-based screening in Beijing using the AI-ExpoStroke model together with established stroke risk factors.
Communities will be randomly assigned to either an AI-driven health management group or a usual community-based health management group. Participants in the AI-driven group will receive continuous health management supported by a digital platform, mobile applications or WeChat-based tools, wearable-device data when available, personalized health guidance, and remote support from community health care providers. Participants in the usual-care group will receive routine community health services, including health examinations, health education, chronic disease follow-up, and medication guidance.
Participants will be followed for 36 months. The main goal is to determine whether AI-driven health management reduces the occurrence of first-ever ischemic stroke. The study will also evaluate transient ischemic attacks, stroke-related disability, mortality, control of major vascular risk factors, adherence to health management, and health economic outcomes.
详细描述
This is an investigator-initiated, multicenter, open-label, stratified cluster-randomized, parallel-group clinical study conducted in community settings in Beijing, China. The study is designed to evaluate the effectiveness, safety, and health economic value of an AI-driven dynamic health management strategy for the primary prevention of ischemic stroke in adults identified as being at high risk of stroke.
Potential participants will be identified from prospective community-based screening programs. Eligibility will be determined using the AI-ExpoStroke risk assessment model together with established stroke "8+2" high-risk factors. The AI-ExpoStroke model was developed and externally validated as part of preceding observational research and is used in the present interventional study primarily for identification and enrollment of individuals at high risk of stroke.
Randomization will be performed at the community level rather than at the individual participant level. Communities will be stratified according to area type, baseline risk-factor profile, and community health service resources, and will then be randomly assigned in a 1:1 ratio to the intervention group or control group. The random allocation sequence will be generated by an independent statistician using SAS or R. Because of the nature of the intervention, the study is open label.
Participants in the intervention group will receive AI-driven remote follow-up and continuous dynamic health management through a stroke prevention and management cloud platform, mobile applications or WeChat-based tools, wearable-device interfaces when applicable, and coordinated support from community health care providers. Participants will generally be encouraged to report health information such as blood pressure, body weight, medication use, and lifestyle-related information at least monthly. The platform will provide individualized risk-management targets, health reminders, lifestyle recommendations, and remote guidance from community health care providers. AI-generated recommendations will be used as supportive management tools and will not replace routine clinical decision-making by physicians.
Participants in the control group will receive usual community-based health management for individuals at high risk of stroke. This includes routine health examinations, basic health education, standard chronic disease follow-up, and medication guidance. Participants in the control group will not receive the dynamic AI-based management service.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Prevention
- 盲法
- None
盲法说明
blank
入排标准
- 年龄范围
- 30 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Age 30 years or older, with no restriction on sex.
- •Permanent resident of Beijing.
- •No previously diagnosed stroke based on prospective community screening.
- •Identified as being at high risk of stroke by the AI-ExpoStroke model in combination with the established stroke "8+2" high-risk factors.
- •Able to comply with study follow-up and willing to provide informed consent.
排除标准
- •Previous diagnosis of ischemic stroke or hemorrhagic stroke.
- •Severe cognitive impairment.
- •Severe organic disease, including malignant tumors, New York Heart Association (NYHA) class III or higher heart failure, renal failure, or other severe conditions.
- •Psychiatric or language impairment that prevents completion of study questionnaires or follow-up.
- •Inability to obtain complete follow-up data or unwillingness to permit access to relevant study data.
研究组 & 干预措施
Arm 1
AI-Driven Dynamic Health Management
干预措施: AI-Driven Dynamic Health Management (Other)
Arm 2
Usual Community-Based Health Management
干预措施: Usual Community-Based Health Management (Other)
结局指标
主要结局
3-Year Cumulative Incidence of First-Ever Ischemic Stroke
时间窗: From randomization through 36 months
The proportion of participants who experience a first-ever ischemic stroke during the 36-month follow-up period. Ischemic stroke will be confirmed based on clinical symptoms and signs together with neuroimaging evidence, including computed tomography or magnetic resonance imaging, according to the predefined endpoint adjudication process.
次要结局
- Cumulative Incidence of First-Ever Transient Ischemic Attack(From randomization through 36 months)
- Proportion of Participants With Disability Following Incident Stroke(From randomization through 36 months)
- All-Cause Mortality(From randomization through 36 months)
- Cardiovascular and Cerebrovascular Mortality(From randomization through 36 months)
- Proportion of Participants Achieving Blood Pressure Control(At 12, 24, and 36 months)