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.
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.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
PREVENTION
Masking
SINGLE
Enrollment
21,689
1. Automatically identifying acute ischemic stroke lesions on DWI. 2. Classification of stroke subtypes and mechanisms. 3. Evidence-based alerts and guidelines for early stroke management. 4. Guideline-recommended secondary stroke prevention strategies.
Beijing Tian tan Hospital
Beijing, Beijing Municipality, China
Incidence rate of new clinical vascular events (ischemic stroke, hemorrhagic stroke, myocardial infarction, or vascular death)
To evaluate the efficacy of AI-based CDSS in reducing the risk of new clinical vascular events (ischemic stroke, hemorrhagic stroke, myocardial infarction, or vascular death) at 3 months after initial symptom onset.
Time frame: 3 months
Incidence rate of new clinical vascular events (ischemic stroke, hemorrhagic stroke, myocardial infarction, or vascular death)
Incidence rate of new clinical vascular events (ischemic stroke, hemorrhagic stroke, myocardial infarction, or vascular death) at discharge, 6,12-months after initial symptom onset.
Time frame: 6, 12 months
Disability
Degree of disability (measured by the Modified Rankin Scale) at discharge, 3, 6, and 12 months
Time frame: 3, 6, 12 months
All-or-none measure of evidence-based performance measures
Proportion of prescription of evidence-based performance measures. "All or none" measures including the evidence-based therapies: antithrombotic medication within 48 hours after symptom onset, dual antiplatelet therapy (aspirin and clopidogrel) started within 24 hours after symptom onset (NIHSS score ≤3), intensive statin therapy, antihypertensive, hypoglycemic medications, dysphagia screening, and deep vein thrombosis prophylaxis within 48 hours of admission. Discharge therapies include: antithrombotics, lipid lowering agents, anticoagulants for atrial fibrilation or flutter, antihypertensive, and hypoglycemic medications.
Time frame: Participants will be followed for the duration of hospital stay, an expected average of 2 weeks
A composite measure score of performance measures
The composite measure score was defined as the total number of eligible performance measures performed divided by the total number of performance measures for which a given patient was eligible.
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Time frame: Participants will be followed for the duration of hospital stay, an expected average of 2 weeks
All-cause mortality
All-cause mortality
Time frame: 3, 6, 12 months