AI-Driven Health Management to Prevent Ischemic Stroke in High-Risk Adults | NCT07779668 | Trialant

Not yet recruitingInterventionalNot ApplicableUpdated Aug 21, 2026

AI-Driven Health Management to Prevent Ischemic Stroke in High-Risk Adults

Intelligent Early Warning of Ischemic Cerebrovascular Disease Based on Multi-Source Data Fusion and Demonstration of Tiered Prevention and Control in Beijing

Overview

Key facts that identify this study and show where it stands today.

ClinicalTrials.gov ID: NCT07779668
Lead Sponsor: Xuanwu Hospital, BeijingOTHER
Overall Status: Not yet recruiting
Study Type: Interventional
Phase: Not Applicable
Enrollment: 26,000Estimated
Last Update Posted: Aug 21, 2026Actual
Start Date: Aug 31, 2026Estimated
Primary Completion Date: Dec 31, 2029Estimated
Completion Date: Dec 31, 2029Estimated

Description

What the study is about and why it is being run, in the researchers' own words.

Official Title: Intelligent Early Warning of Ischemic Cerebrovascular Disease Based on Multi-Source Data Fusion and Demonstration of Tiered Prevention and Control in Beijing
Brief 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 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.

Conditions

The medical conditions and diseases this study investigates.

Ischemic Stroke

Interventions

The treatments and procedures being evaluated in this study.

Usual Community-Based Health Management
AI-Driven Dynamic Health Management

Participation

Who can join this study and the criteria used to decide.

Ages Eligible for Study: Adult (18-64)Older Adult (65+)
Sexes Eligible for Study: All
Accepts Healthy Volunteers: No
Eligibility Criteria: 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, heart failure of NYHA class III or higher, 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.

Design

How the study is designed and how its participant groups are organized.

Primary Purpose: Prevention
Allocation: Randomized
Interventional Model: Parallel Assignment
Masking: None (Open Label)

Contacts

The people to reach with questions about this study or how to join it.

No central contacts

There are no central contacts for this study.

Locations

Where the study is run and whether each site is currently recruiting.

No locations

There are no locations for this study.

Collaborators

The organizations and people involved in this study.

Lead Sponsor: Xuanwu Hospital, BeijingOTHER

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Other registered studies that closely resemble this one.

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.