AI-Driven Health Management to Prevent Ischemic Stroke in High-Risk Adults (AI-ExpoStroke)
Intelligent Early Warning of Ischemic Cerebrovascular Disease Based on Multi-Source Data Fusion and Demonstration of Tiered Prevention and Control in Beijing
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.
Study Overview
Status
Conditions
Intervention / Treatment
Detailed Description
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.
All participants will be followed for 36 months, with formal follow-up assessments at 12, 24, and 36 months. Suspected stroke, transient ischemic attack, hospitalization, and death will be evaluated when they occur. Endpoint events will be verified using relevant clinical information and, when applicable, neuroimaging findings and medical records according to a predefined endpoint adjudication process.
The primary objective is to determine whether AI-driven dynamic health management reduces the 3-year cumulative incidence of first-ever ischemic stroke compared with usual community-based health management. Secondary evaluations include transient ischemic attack, stroke-related disability, mortality, vascular risk-factor control, adherence to intervention and follow-up, improvement in stroke-prevention knowledge, and health economic outcomes.
The study does not involve an investigational drug, an invasive investigational device, or collection of additional research-specific biological samples. Laboratory and clinical information used in the study will primarily be obtained from examinations performed as part of routine clinical care, routine physical examinations, or standard community health management.
Study Type
Enrollment (Estimated)
Phase
- Not Applicable
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Description
Inclusion Criteria:
- 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.
Exclusion Criteria:
- 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.
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Prevention
- Allocation: Randomized
- Interventional Model: Parallel Assignment
- Masking: None (Open Label)
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
|---|---|
|
Experimental: Arm 1
AI-Driven Dynamic Health Management
|
A comprehensive AI-supported health management strategy for adults at high risk of stroke.
The intervention integrates the AI-ExpoStroke risk assessment system with a digital stroke prevention and management platform, mobile applications or WeChat-based tools, wearable-device interfaces when applicable, and coordinated community health care.
Participants generally report blood pressure, body weight, medication use, and lifestyle-related information at least monthly.
The platform supports dynamic risk assessment, individualized risk-factor management targets, health reminders, lifestyle recommendations, health education, and remote guidance from community health care providers.
AI-generated recommendations are intended to support health management and do not replace clinical decision-making by physicians.
|
|
Active Comparator: Arm 2
Usual Community-Based Health Management
|
Usual community-based health management for adults at high risk of stroke, including routine health examinations, basic health education, standard chronic disease follow-up, and routine medication guidance according to community health care practice.
Participants do not receive the AI-driven dynamic health management service provided through the AI-ExpoStroke platform.
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
3-Year Cumulative Incidence of First-Ever Ischemic Stroke
Time Frame: 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.
|
From randomization through 36 months
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Cumulative Incidence of First-Ever Transient Ischemic Attack
Time Frame: From randomization through 36 months
|
The proportion of participants who experience a first-ever transient ischemic attack (TIA) during the follow-up period, based on clinical evaluation and relevant medical records according to the predefined endpoint adjudication process.
|
From randomization through 36 months
|
|
Proportion of Participants With Disability Following Incident Stroke
Time Frame: From randomization through 36 months
|
The proportion of participants with an incident stroke who are assessed as having stroke-related disability during follow-up.
Disability status will be evaluated at endpoint assessment, and the modified Rankin Scale (mRS) will be recorded for participants with stroke-related disability.
|
From randomization through 36 months
|
|
All-Cause Mortality
Time Frame: From randomization through 36 months
|
The proportion of participants who die from any cause during the 36-month follow-up period.
Deaths will be verified using available medical records, discharge summaries, death certificates, or other relevant records.
|
From randomization through 36 months
|
|
Cardiovascular and Cerebrovascular Mortality
Time Frame: From randomization through 36 months
|
The proportion of participants who die from cardiovascular or cerebrovascular causes during the 36-month follow-up period.
Cause of death will be determined using available clinical records and the predefined endpoint adjudication process.
|
From randomization through 36 months
|
|
Proportion of Participants Achieving Blood Pressure Control
Time Frame: At 12, 24, and 36 months
|
The proportion of participants who meet the predefined blood pressure control target at follow-up assessments.
|
At 12, 24, and 36 months
|
Collaborators and Investigators
Sponsor
Study record dates
Study Major Dates
Study Start (Estimated)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- AI-ExpoStroke-001
- 2026-2G-20111 (Other Grant/Funding Number: Capital's Funds for Health Improvement and Research)
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.