NCT07727590尚未招募不适用
Multimodal Visual Language Model-Assisted Diagnostic Strategy in the Emergency Department: A Prospective Multicenter Randomized Controlled Trial (ER-VISION-AI Study)
适应症
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 1,000
- 试验地点
- 1
- 主要终点
- Diagnostic concordance between the final emergency department diagnosis and the blinded adjudicated reference diagnosis established at hospital discharge.
研究概览
简要总结
Prospective, multicenter, randomized, open-label, blinded-endpoint (PROBE-like) clinical trial evaluating whether physician-supervised Generative Pre-trained Transformer (GPT)-assisted multimodal diagnostic support improves diagnostic concordance in emergency department patients presenting with acute cardiopulmonary symptoms.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Diagnostic
- 盲法
- None
盲法说明
Outcome assessor blinded
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Age ≥18 years
- •Presentation to a participating emergency department with acute cardiopulmonary symptoms, including chest pain, dyspnea, palpitations, syncope, dizziness, or fever accompanied by cardiopulmonary symptoms
- •Performance of both a standard 12-lead electrocardiogram and chest radiography during the initial emergency department evaluation
- •Availability of initial clinical assessment, vital signs, laboratory findings, and all mandatory clinical information required for the multimodal AI workflow
- •Expected emergency department observation or hospital admission for at least 24 hours
- •Ability and willingness to provide written informed consent
排除标准
- •Inability or refusal to provide written informed consent
- •Requirement for immediate life-saving intervention that precludes completion of the study workflow
- •Death before completion of the initial emergency department diagnostic assessment
- •Electrocardiographic quality insufficient for reliable physician or Artificial intelligence (AI) interpretation
- •Chest radiographic quality insufficient for reliable physician or Artificial intelligence (AI) interpretation
- •Cardiac pacing rhythm
- •Missing mandatory clinical information required for the multimodal Artificial intelligence (AI) workflow
- •Previous enrollment in the ER-VISION-AI trial
- •Inability to establish a blinded adjudicated reference diagnosis
结局指标
主要结局
Diagnostic concordance between the final emergency department diagnosis and the blinded adjudicated reference diagnosis established at hospital discharge.
时间窗: During the index hospitalization, up to hospital discharge (average 3 days)
Diagnostic concordance between the treating physician's final emergency department diagnosis and the blinded adjudicated reference diagnosis based on the prespecified principal diagnostic category.
次要结局
- Diagnostic concordance after Generative Pre-trained Transformer (GPT)-assisted diagnostic support(During the index emergency department visit (average 6 hours))
- Time from emergency department presentation to final diagnosis(During the index emergency department visit (average 6 hours))
- Diagnostic reclassification after Generative Pre-trained Transformer (GPT)-assisted evaluation(During the index emergency department visit (average 6 hours))
- Physician diagnostic confidence(During the index emergency department visit (average 6 hours))
- Physician acceptance of Generative Pre-trained Transformer (GPT)-generated diagnostic recommendations(During the index emergency department visit (average 6 hours))
- Emergency department disposition accuracy(Up to hospital discharge (average 3 days))
- Emergency department length of stay(Up to hospital discharge (average 3 days))
- Hospital length of stay(Up to hospital discharge (average 3 days))
- In-hospital mortality(Up to hospital discharge (average 3 days))
- 30-day all-cause mortality(30 days)
- 30-day emergency department revisit(30 days)
- 30-day hospital readmission(30 days)
研究者
研究点 (1)
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