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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| AI-based CDSS | Experimental |
|
|
| Usual Care | No Intervention | Usual Care |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| AI-based CDSS | Device |
|
| Measure | Description | Time Frame |
|---|---|---|
| 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. | 3 months |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | 6, 12 months |
| Measure | Description | Time Frame |
|---|---|---|
| Moderate and severe bleeding events according to the GUSTO criteria | Severe bleeding incidence (GUSTO definition), including fatal bleeding and symptomatic intracranial hemorrhage. | 3, 6, 12 months |
| All bleeding events |
Inclusion Criteria:
Cluster Inclusion Criteria:
Patient Inclusion Criteria:
Exclusion Criteria:
Cluster Exclusion Criteria:
Patient Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Zixiao Li, MD | Contact | 00861013683234256 | lizixiao2008@hotmail.com | |
| Lingling Ding, MD | Contact | 00861013552358752 | dll_ing@sina.com |
| Name | Affiliation | Role |
|---|---|---|
| Yongjun Wang, MD | Beijing Tian Tan Hospital, Capital Medical University, Beijing, China | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Beijing Tian tan Hospital | Beijing | Beijing Municipality | 100070 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 29959443 | Background | Wang Y, Li Z, Zhao X, Wang C, Wang X, Wang D, Liang L, Liu L, Wang C, Li H, Shen H, Bettger J, Pan Y, Jiang Y, Yang X, Zhang C, Han X, Meng X, Yang X, Kang H, Yuan W, Fonarow GC, Peterson ED, Schwamm LH, Xian Y, Wang Y; GOLDEN BRIDGE-AIS Investigators. Effect of a Multifaceted Quality Improvement Intervention on Hospital Personnel Adherence to Performance Measures in Patients With Acute Ischemic Stroke in China: A Randomized Clinical Trial. JAMA. 2018 Jul 17;320(3):245-254. doi: 10.1001/jama.2018.8802. | |
| 41862204 |
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| ID | Term |
|---|---|
| D020521 | Stroke |
| D000083242 | Ischemic Stroke |
| ID | Term |
|---|---|
| D002561 | Cerebrovascular Disorders |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |
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| Disability |
Degree of disability (measured by the Modified Rankin Scale) at discharge, 3, 6, and 12 months |
| 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. | 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. | Participants will be followed for the duration of hospital stay, an expected average of 2 weeks |
| All-cause mortality | All-cause mortality | 3, 6, 12 months |
All bleeding events (severe/moderate bleeding and intracranial hemorrhage)
| 3, 6, 12 months |
| Intracranial hemorrhagic events | Intracranial hemorrhagic events | 3, 6, 12 months |
| Total costs of care | Total costs of care | Participants will be followed for the duration of hospital stay, an expected average of 2 weeks |
| Average length of stay | Average length of stay | Participants will be followed for the duration of hospital stay, an expected average of 2 weeks |
| Home time | We calculated home time as total days alive and not in a hospital or skilled nursing facility. | 3, 6, 12 months |
| All-cause readmission | All-cause readmission | 3, 6, 12 months |
| Ischemic stroke readmission | Ischemic stroke readmission | 3, 6, 12 months |
| Hemorrhagic stroke readmission | Hemorrhagic stroke readmission | 3, 6, 12 months |
| Cardiovascular readmission | Cardiovascular readmission | 3, 6, 12 months |
| Adverse events | Adverse events | 3, 6, 12 months |
| Stratified analysis | Efficacy endpoint will also be analyzed stratified by Hospital level(secondary hospitals/tertiary hospitals), economic-geographical regions (eastern, central, and western), stroke severity (NIHSS≤3/NIHSS>3) and stroke subtypes. | 3, 6, 12 months |
| Derived |
| Zhang X, Ding L, Jing J, Wang C, Gu H, Jiang Y, Meng X, Liu T, Xie X, Xu M, Hu M, Zhang Y, Fu H, Liu P, Du C, Du K, Wang M, Li H, Gong X, Dong K, Xiong Y, Wang Y, Liu L, Zhang Z, Zang Y, Yang C, Xian Y, Peterson E, Fonarow GC, Schwamm LH, Zhao X, Wang Y, Li Z; GOLDEN BRIDGE II Investigators. Effect of a clinical decision support system on stroke care quality and outcomes in patients with acute ischaemic stroke (GOLDEN BRIDGE II): cluster randomised clinical trial. BMJ. 2026 Mar 20;392:e085810. doi: 10.1136/bmj-2025-085810. |
| 37699726 | Derived | Li Z, Zhang X, Ding L, Jing J, Gu HQ, Jiang Y, Meng X, Du C, Wang C, Wang M, Xu M, Zhang Y, Hu M, Li H, Gong X, Dong K, Zhao X, Wang Y, Liu L, Xian Y, Peterson E, Fonarow GC, Schwamm LH, Wang Y. Rationale and design of the GOLDEN BRIDGE II: a cluster-randomised multifaceted intervention trial of an artificial intelligence-based cerebrovascular disease clinical decision support system to improve stroke outcomes and care quality in China. Stroke Vasc Neurol. 2024 Dec 30;9(6):723-729. doi: 10.1136/svn-2023-002411. |
| D014652 | Vascular Diseases |
| D002318 | Cardiovascular Diseases |