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

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.