Construction and Evaluation of an Intelligent Decision System for Reperfusion Therapy in Acute Ischemic Stroke

Overview

This multicenter, cluster-randomized controlled trial will evaluate the effectiveness and safety of the LingBao System, an AI-enabled clinical decision support platform for reperfusion therapy in acute ischemic stroke (AIS). Twenty certified stroke centers will be randomized 1:1 to LingBao-assisted care or standard care. Consecutive patients aged 18 years or older who present within 24 hours of symptom onset or last-known-well and are evaluated for intravenous thrombolysis and/or endovascular therapy will be prospectively enrolled. At intervention sites, clinicians may use the LingBao System during their routine workflows. The platform integrates routinely available clinical and imaging data, automatically estimates onset-to-treatment windows, screens contraindications, and provides evidence-based, guideline-concordant recommendations for reperfusion therapy; all treatment decisions remain at physician discretion. The primary endpoint is the 90-day modified Rankin Scale (mRS) score analyzed by ordinal shift. Secondary endpoints include workflow metrics (door-to-needle time and door-to-puncture time), reperfusion treatment rates, early neurological improvement, symptomatic intracranial hemorrhage, and mortality. The study plans to include approximately 20 centers (about 150 patients per center), accounting for intracluster correlation. The findings will provide real-world evidence on the clinical value of AI-assisted decision support for reperfusion therapy in AIS and inform broader implementation of intelligent stroke management systems.

The LingBao System (Stroke Reperfusion Intelligent Decision System, SRIDS) is an AI-based clinical decision support platform designed to assist physicians in the evaluation and treatment decision-making process for acute ischemic stroke (AIS) patients eligible for reperfusion therapy. Built upon the 2024 Chinese Guidelines for Reperfusion Therapy in Acute Ischemic Stroke, major international randomized controlled trial (RCT) evidence, and large-scale real-world datasets, LingBao integrates clinical information, imaging parameters, and guideline-based criteria to provide transparent, evidence-graded recommendations for intravenous thrombolysis and/or endovascular therapy. This multicenter, cluster-randomized controlled trial aims to assess the real-world effectiveness and safety of LingBao in optimizing reperfusion decision-making and improving patient outcomes. Twenty certified stroke centers in China will be randomized 1:1 to either LingBao-assisted care or standard care. Consecutive adult patients (≥18 years) presenting within 24 hours of last-known-well and evaluated for reperfusion therapy will be prospectively enrolled. At intervention sites, clinicians may use LingBao during standard clinical workflows. The system automatically calculates the estimated onset-to-treatment window, screens contraindications, summarizes imaging-based eligibility criteria, and displays guideline-concordant recommendations along with their class and level of evidence. LingBao does not replace physician judgment; all clinical decisions remain entirely at the discretion of the treating team. Data on patient demographics, baseline characteristics, workflow times, treatments, and outcomes will be collected through standardized electronic case report forms (eCRFs). The primary outcome is functional status at 90 days, measured by the modified Rankin Scale (mRS) and analyzed using an ordinal shift model. Key secondary outcomes include door-to-needle time (DNT) for intravenous thrombolysis, door-to-puncture time (DPT) for endovascular therapy, rates of reperfusion treatment, early neurological improvement, symptomatic intracranial hemorrhage, and mortality. By systematically integrating AI-driven clinical reasoning with evidence-based medicine, the LingBao study aims to establish an intelligent, reproducible, and guideline-concordant framework for acute stroke management. The results are expected to inform large-scale implementation of AI-supported decision systems to enhance the quality, consistency, and efficiency of stroke reperfusion therapy in real-world practice.