Ischemic StrokeAI-ExpoStrokeNCT07779668
AI-Driven Health Management to Prevent Ischemic Stroke in High-Risk Adults
A study testing Usual Community-Based Health Management and AI-Driven Dynamic Health Management in people with Ischemic Stroke, run by Xuanwu Hospital, Beijing.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 30 to 80 years
- Conditions the study is for
- Ischemic Stroke
- What is being tested?
- Usual Community-Based Health Management and AI-Driven Dynamic Health Management
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 26,000 participants
- Where
- No sites listed yet
- Study dates
- 31 Aug 2026to 31 Dec 2029
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 30 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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.
Who cannot join
- 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.
About this study
Official title: 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.
Read the 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.
- Conditions
- Ischemic Stroke
- Being tested
- Usual Community-Based Health Management, AI-Driven Dynamic Health Management
- Keywords
- Artificial Intelligence, Stroke Prevention, Primary Prevention
- Medical subject headings
- Ischemic Stroke
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Usual Community-Based Health ManagementOther
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.
AI-Driven Dynamic Health ManagementOther
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.
| Study group | Role | What they receive |
|---|---|---|
| Arm 1 | Experimental | AI-Driven Dynamic Health Management Receives: Other: AI-Driven Dynamic Health Management |
| Arm 2 | Active comparator | Usual Community-Based Health Management Receives: Other: Usual Community-Based Health Management |
What is being measured
The primary outcome is the main question the study is designed to answer.
3-Year Cumulative Incidence of First-Ever Ischemic Stroke
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.
Measured: From randomization through 36 months
Secondary outcomes (5)
Cumulative Incidence of First-Ever Transient Ischemic Attack
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.
Measured: From randomization through 36 months
Proportion of Participants With Disability Following Incident Stroke
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.
Measured: From randomization through 36 months
All-Cause Mortality
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.
Measured: From randomization through 36 months
Cardiovascular and Cerebrovascular Mortality
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.
Measured: From randomization through 36 months
Proportion of Participants Achieving Blood Pressure Control
The proportion of participants who meet the predefined blood pressure control target at follow-up assessments.
Measured: At 12, 24, and 36 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Aug 2026
- Study start31 Aug 2026 (estimated)
- Primary completion31 Dec 2029 (estimated)
- Study completion31 Dec 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Ischemic StrokeStudies of the same condition, open and under way.
- More trials run by Xuanwu Hospital, BeijingThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- This is a stratified cluster-randomized, parallel-group study. The unit of randomization is the community rather than the individual participant. Communities are stratified according to area type, baseline risk-factor level, and community health service resources, and are then randomly assigned in a 1:1 ratio to the AI-driven dynamic health management group or the usual community-based health management group. The random allocation sequence is generated by an independent statistician using SAS or R.
- Masking
- None (open label)
- Enrolment
- 26,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Xuanwu Hospital, Beijing
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07779668
- Sponsor's protocol ID
- AI-ExpoStroke-001
- Other IDs
- 2026-2G-20111
- Acronym
- AI-ExpoStroke
- Status last verified
- Aug 2026
- First submitted
- 18 Aug 2026
- Last updated
- 2 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be shared publicly because the study involves sensitive health information collected from a large community-based population. Study data will be de-identified, securely stored, and accessed only by authorized study personnel in accordance with the approved study protocol, institutional ethics requirements, and applicable data protection regulations. Aggregate study results may be disseminated through scientific publications and reports.
Study at a glance
From the registry record, last updated by the sponsor on 2 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Intelligent Early Warning of Ischemic Cerebrovascular Disease Based on Multi-Source Data Fusion and Demonstration of Tiered Prevention and Control in Beijing
- ClinicalTrials.gov ID
- NCT07779668
- Acronym / sponsor ID
- AI-ExpoStroke / AI-ExpoStroke-001
- Conditions
- Ischemic Stroke
- Keywords
- Artificial Intelligence, Stroke Prevention, Primary Prevention
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 26,000 participants (estimated)
- Lead sponsor
- Xuanwu Hospital, BeijingUniversity, hospital or other
- Study start
- 31 Aug 2026 (estimated)
- Primary completion
- 31 Dec 2029 (estimated)
- Study completion
- 31 Dec 2029 (estimated)
- First posted
- 21 Aug 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- This is a stratified cluster-randomized, parallel-group study. The unit of randomization is the community rather than the individual participant. Communities are stratified according to area type, baseline risk-factor level, and community health service resources, and are then randomly assigned in a 1:1 ratio to the AI-driven dynamic health management group or the usual community-based health management group. The random allocation sequence is generated by an independent statistician using SAS or R.
| Arm | Type | Receives |
|---|---|---|
| Arm 1 | Experimental | Other: AI-Driven Dynamic Health ManagementAI-Driven Dynamic Health Management |
| Arm 2 | Active comparator | Other: Usual Community-Based Health ManagementUsual Community-Based Health Management |
Who may qualify
- Ages
- 30 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (5 listed)
- 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.
Key exclusion criteria (5 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Usual Community-Based Health ManagementUsual 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. | Other | Arm 2 |
| AI-Driven Dynamic Health ManagementA 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. | Other | Arm 1 |
Primary outcome
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.
5 secondary outcomes
Cumulative Incidence of First-Ever Transient Ischemic Attack
Time frame: From randomization through 36 months
Proportion of Participants With Disability Following Incident Stroke
Time frame: From randomization through 36 months
All-Cause Mortality
Time frame: From randomization through 36 months
Cardiovascular and Cerebrovascular Mortality
Time frame: From randomization through 36 months
Proportion of Participants Achieving Blood Pressure Control
Time frame: At 12, 24, and 36 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Sep 2026.
- Individual participant dataSharing planned: No. Individual participant data will not be shared publicly because the study involves sensitive health information collected from a large community-based population. Study data will be de-identified, securely stored, and accessed only by authorized study personnel in accordance with the approved study protocol, institutional ethics requirements, and applicable data protection regulations. Aggregate study results may be disseminated through scientific publications and reports.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
A study testing Usual Community-Based Health Management and AI-Driven Dynamic Health Management in people with Ischemic Stroke, run by Xuanwu Hospital, Beijing.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 2 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
3-Year Cumulative Incidence of First-Ever Ischemic Stroke (time frame: From randomization through 36 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 2 Sep 2026.