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临床试验/NCT07810686
NCT07810686招募中不适用

AI & Prehospital ECG Analysis: A Randomized Controlled Trial of a Smartphone Large Language Model for Occlusion Myocardial Infarction Detection by Prehospital Providers

École Supérieure de Soins Ambulanciers - College of Higher Education in Prehospital Care1 个研究点 分布在 1 个国家目标入组 144 人开始时间: 2026年9月2日最近更新:
适应症
干预措施

试验速览

阶段
不适用
状态
招募中
发起方
入组人数
144
试验地点
1
主要终点
Proportion of vignettes with correct identification of occlusion myocardial infarction (OMI) status

研究概览

简要总结

Prehospital providers interpret 12-lead electrocardiograms (ECGs) under time pressure and without immediate expert support. Missed acute coronary occlusion - occlusion myocardial infarction (OMI) - delays reperfusion, while false positive interpretations trigger unnecessary catheterization laboratory activations. Multimodal large language models (LLMs) available on any smartphone can now analyze a photographed ECG, and prehospital providers have begun using them spontaneously. No randomized trial has evaluated whether this practice improves diagnostic performance.

This randomized controlled trial compares the diagnostic performance of prehospital providers interpreting ECG clinical vignettes with and without mandatory assistance from a single, version-locked smartphone large language model. Participants - paramedics, emergency medical technicians, nurses and physicians practicing in prehospital care in French-speaking Switzerland - are randomized 1:1 on a dedicated digital platform and answer 14 clinical vignettes presented in individually randomized order. Each vignette is built around a real, anonymized 12-lead ECG obtained during routine clinical care.

The primary outcome is the proportion of vignettes for which the participant correctly identifies the presence or absence of an OMI. Secondary outcomes are sensitivity, specificity, and the accuracy of the prehospital priority decision level.

详细描述

Design and setting. Two-arm parallel-group randomized controlled trial conducted on a dedicated digital platform. Phase 1 takes place during a single in-person session at the Swiss French-speaking prehospital clinical research conference (Morat, Switzerland, 2 September 2026). Phase 2 extends recruitment to prehospital emergency services across French-speaking Switzerland (September to November 2026) using an identical standardized protocol.

Randomization. Individual 1:1 allocation performed automatically by the platform using permuted blocks of variable size (4 to 6), without stratification, after the demographic questionnaire and before the first vignette. Allocation cannot be changed once assigned. The presentation order of the 14 vignettes is independently randomized for each participant, which neutralizes position effects and prevents copying between neighbouring participants.

Intervention. Participants allocated to the intervention arm must consult the study-imposed large language model for every vignette before submitting their answer; the platform locks the submit button until use of the tool is confirmed. A single model (OpenAI GPT-4o) in an API version locked for the entire study is used by all participants, with a standardized, non-modifiable prompt identical for every participant and every vignette. Participants cannot add text, ask follow-up questions, or provide additional clinical context. Exposure to the tool is mandatory, but adherence to its interpretation is not: participants remain free to base their final answer on their own clinical reasoning. Control participants interpret the same ECGs unaided, with smartphones turned face down and out of reach.

Reference standard. For each vignette, the expected answers were defined a priori by the study cardiologist and locked, with any subsequent modification time-stamped in the platform audit log. The reference standard is the answer expected of a prehospital provider at the point of care, anchored on coronary angiography wherever angiography is discriminant. For non-ischaemic mimics, the expected answer depends on whether the acute presentation allows the condition to be distinguished from a coronary occlusion: it does not for the Takotsubo case included in the study, whose expected answer is therefore "yes", whereas acute pericarditis, being usually recognizable, has an expected answer of "no". This pre-specified departure from a purely angiographic standard is reported as such, and the primary analysis is repeated in a sensitivity analysis in which all mimics are classified as non-OMI.

Blinding. Participants and investigators cannot be masked. The statistician conducting the primary analysis receives coded group labels only, and the allocation key is released after database lock and approval of the statistical analysis plan.

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
Single (Outcomes Assessor)

盲法说明

Participants and investigators cannot be masked to allocation, since participants in the intervention arm knowingly use the AI tool. The statistician conducting the primary analysis is masked: groups are coded "Group 1" and "Group 2", and the allocation key is held solely by the principal investigator and released only after database lock and approval of the statistical analysis plan.

入排标准

年龄范围
18 Years 至 —(Adult, Older Adult)
性别
All
接受健康志愿者

入选标准

  • Prehospital care provider practising in French-speaking Switzerland
  • Any level of training: emergency medical technician, paramedic (ES), nurse (ES/HES) in prehospital emergency care, or prehospital emergency physician
  • Electronic informed consent signed before randomization

排除标准

  • Cardiologist
  • Any person not practising in prehospital care
  • Insufficient command of written French to answer the vignettes reliably
  • Refusal to participate or withdrawal of consent

研究组 & 干预措施

Control: unaided ECG interpretation

No Intervention

Participants interpret each of the 14 ECG vignettes without any assistance. Smartphones are turned face down and out of reach for the duration of the session. No intervention is administered.

AI-assisted ECG interpretation

Experimental

Participants must consult the study-imposed large language model for every vignette before submitting their answer. The platform locks the submit button until use of the tool is confirmed. Participants remain free not to follow the interpretation produced by the model and may base their final answer on their own clinical reasoning.

干预措施: Smartphone large language model assistance (GPT-4o, version-locked) (Diagnostic Test)

结局指标

主要结局

Proportion of vignettes with correct identification of occlusion myocardial infarction (OMI) status

时间窗: Single study session, approximately 90 minutes; 14 vignettes per participant

For each of the 14 vignettes, the participant answers a binary question: "At this stage of care, is an OMI (acute coronary occlusion) likely? Yes / No". Responses are scored against a reference standard defined a priori, vignette by vignette, by the study cardiologist and locked before data collection. This reference standard is the answer expected of a prehospital provider at the point of care, anchored on coronary angiography wherever angiography is discriminant. For non-ischaemic mimics the expected answer depends on whether the acute presentation allows the condition to be distinguished from a coronary occlusion: it does not for the Takotsubo case (expected answer "yes"), whereas acute pericarditis is usually recognisable (expected answer "no"). This pre-specified departure from a purely angiographic standard is reported as such, and the analysis is repeated in a sensitivity analysis classifying all mimics as non-OMI. The outcome is the proportion of correctly classified vignettes

次要结局

  • Specificity of OMI detection(Single study session, approximately 90 minutes)
  • Sensitivity of OMI detection(Single study session, approximately 90 minutes)
  • Accuracy of the prehospital priority decision level(Single study session, approximately 90 minutes)

研究者

发起方
École Supérieure de Soins Ambulanciers - College of Higher Education in Prehospital Care
申办方类型
Other
责任方
Principal Investigator
主要研究者

Laurent Bourgeois

Maître-adjoint (Lecturer and Deputy Head of School), Principal Investigator

École Supérieure de Soins Ambulanciers - College of Higher Education in Prehospital Care

研究点 (1)

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