ER-VISION-AI Study

Официальное название

Multimodal Visual Language Model-Assisted Diagnostic Strategy in the Emergency Department: A Prospective Multicenter Randomized Controlled Trial (ER-VISION-AI Study)

Обзор

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.

Вмешательства

• Диагностический тест Generative Pre-trained Transformer (GPT)-assisted multimodal visual language model (VLM) diagnostic support
  A Generative Pre-trained Transformer (GPT)-based multimodal visual language model integrates electrocardiograms, chest radiographs, structured clinical information, laboratory findings, vital signs, and relevant clinical history to generate diagnostic suggestions and differential diagnoses for physician-supervised clinical decision support.
• Диагностический тест Conventional emergency department diagnostic evaluation
  Routine emergency department diagnostic evaluation performed according to standard clinical practice without AI-assisted diagnostic support.

Первичные конечные точки

• Diagnostic concordance between the final emergency department diagnosis and the blinded adjudicated reference diagnosis established at hospital discharge. [Срок оценки: During the index hospitalization, up to hospital discharge (average 3 days)]

Вторичные конечные точки (12)

• Diagnostic concordance after Generative Pre-trained Transformer (GPT)-assisted diagnostic support [Срок оценки: During the index emergency department visit (average 6 hours)]
• Time from emergency department presentation to final diagnosis [Срок оценки: During the index emergency department visit (average 6 hours)]
• Diagnostic reclassification after Generative Pre-trained Transformer (GPT)-assisted evaluation [Срок оценки: During the index emergency department visit (average 6 hours)]
• Physician diagnostic confidence [Срок оценки: During the index emergency department visit (average 6 hours)]
• Physician acceptance of Generative Pre-trained Transformer (GPT)-generated diagnostic recommendations [Срок оценки: During the index emergency department visit (average 6 hours)]
• Emergency department disposition accuracy [Срок оценки: Up to hospital discharge (average 3 days)]
• Emergency department length of stay [Срок оценки: Up to hospital discharge (average 3 days)]
• Hospital length of stay [Срок оценки: Up to hospital discharge (average 3 days)]
• In-hospital mortality [Срок оценки: Up to hospital discharge (average 3 days)]
• 30-day all-cause mortality [Срок оценки: 30 days]
• 30-day emergency department revisit [Срок оценки: 30 days]
• 30-day hospital readmission [Срок оценки: 30 days]

Критерии участия

Критерии включения

• 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

Критерии исключения

• 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

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Распределение

Рандомизированное

Модель

Параллельные группы

Маскирование

Открытое

Основная цель

Диагностика

Центры проведения

South Korea · 1 центр

• Ewha Womans University Mokdong Hospital — Seoul

Публикации

• Lopez-Puerta JM, Fernandez-Marin MR, Martin Benlloch JA, Lorente R. Spinal osteoid osteoma recurring as an aggressive osteoblastoma. Neurocirugia (Engl Ed). 2020 May-Jun;31(3):146-150. doi: 10.1016/j.neucir.2019.06.002. Epub 2019 Sep 2. English, Spanish. PMID 31488355
• ANCA-associated vasculitis. Nat Rev Dis Primers. 2020 Aug 27;6(1):72. doi: 10.1038/s41572-020-0212-y. No abstract available. PMID 32855427
• Kim TH, Kim CH, Choi SG. Radiation-induced angiosarcoma (RIAS) of the maxilla: a case report. J Korean Assoc Oral Maxillofac Surg. 2020 Aug 31;46(4):288-291. doi: 10.5125/jkaoms.2020.46.4.288. PMID 32855377
• Li R, Chen X, Wang Y. Adverse events analysis of Relugolix (Orgovyx(R)) for prostate cancer based on the FDA Adverse Event Reporting System (FAERS). PLoS One. 2024 Oct 22;19(10):e0312481. doi: 10.1371/journal.pone.0312481. eCollection 2024. PMID 39436909
• Asravor RK. Uncovering the forgotten story of the impact of Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome on economic growth in Ghana: A gender analysis. Int J Health Plann Manage. 2023 Sep;38(5):1495-1509. doi: 10.1002/hpm.3675. Epub 2023 Jun 23. PMID 37353922
• Shakiba M, Nazemipour M, Mansournia N, Mansournia MA. Protective effect of intensive glucose lowering therapy on all-cause mortality, adjusted for treatment switching using G-estimation method, the ACCORD trial. Sci Rep. 2023 Apr 10;13(1):5833. doi: 10.1038/s41598-023-32855-3. PMID 37037931
• Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019 Jan;25(1):44-56. doi: 10.1038/s41591-018-0300-7. Epub 2019 Jan 7. PMID 30617339
• Hsu HW, Chiu MC, Shoemaker D, Yang CS. Viral infections in fire ants lead to reduced foraging activity and dietary changes. Sci Rep. 2018 Sep 10;8(1):13498. doi: 10.1038/s41598-018-31969-3. PMID 30202033

Идентификаторы

NCT: NCT07727590 · ER-VISION-AI study

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

Открыть это исследование на ClinicalTrials.gov ↗