Official Title

A Multi-centre, Cluster Randomized Controlled Trial of an Artificial Intelligence-Based Clinical Decision Support System for Integrated Management of Patients With Acute Ischemic Stroke: The GOLDEN BRIDGE II Trial（GOLDEN BRIDGE II ）

Brief Summary

This study will conduct a cluster randomized controlled trial to evaluate the impact of an artificial intelligence-based clinical decision support system for the integrated management of patients with acute ischemic stroke on the adherence to guideline-based therapies and the incidence of new clinical vascular events.

Detailed Description

Artificial intelligence (AI) and clinical decision support system (CDSS) have demonstrated great progress in the diagnosis and treatment of cerebrovascular diseases.CDSS can use computer technology to simulate and extend expert knowledge and empirical evidence in a timely and efficient manner. The combination of AI and CDSS is a potential solution to the shortage of medical resources and can improve the quality of and promote the standardization of medical services. At intervention sites, neurologists will receive support on use of the AI-based CDSS.

Aim: To evaluate the effectiveness of an AI-based CDSS for stroke management in patients with acute ischemic stroke within 7 days of symptom onset.

Intervention: An AI-based CDSS for an integrated management of patients with acute ischemic stroke. The strategy includes automatically identifying acute stroke lesions and lesion patterns, automated classification of stroke subtypes and mechanisms, evidence-based alerts, and guideline-recommended secondary stroke prevention strategies.

Eighty eligible hospitals in china, stratified by hospital capacity (secondary grade or tertiary) and economic-geographical regions (eastern, central, and western), will be randomized into either the CDSS group or the usual care group.

Principal Investigator

Yongjun Wang

Study Start (Actual)

2020-09-01

Primary Completion (Estimated)

2022-09-30

Study Completion (Estimated)

2023-06-30

Enrollment (Estimated)

21689