Department of Emergency Medicine, Division of Pediatric Emergency Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, New York, USA
Departments of Emergency Medicine and Pediatrics, University of California Davis School of Medicine, University of California Davis Health, Sacramento, California, USA.
BMJ Open. 2023 Nov 22;13(11):e079040. doi: 10.1136/bmjopen-2023-079040.
Headache is a common chief complaint of children presenting to emergency departments (EDs). Approximately 0.5%-1% will have emergent intracranial abnormalities (EIAs) such as brain tumours or strokes. However, more than one-third undergo emergent neuroimaging in the ED, resulting in a large number of children unnecessarily exposed to radiation. The overuse of neuroimaging in children with headaches in the ED is driven by clinician concern for life-threatening EIAs and lack of clarity regarding which clinical characteristics accurately identify children with EIAs. The study objective is to derive and internally validate a stratification model that accurately identifies the risk of EIA in children with headaches based on clinically sensible and reliable variables.
Prospective cohort study of 28 000 children with headaches presenting to any of 18 EDs in the Pediatric Emergency Care Applied Research Network (PECARN). We include children aged 2-17 years with a chief complaint of headache. We exclude children with a clear non-intracranial alternative diagnosis, fever, neuroimaging within previous year, neurological or developmental condition such that patient history or physical examination may be unreliable, Glasgow Coma Scale score<14, intoxication, known pregnancy, history of intracranial surgery, known structural abnormality of the brain, pre-existing condition predisposing to an intracranial abnormality or intracranial hypertension, head injury within 14 days or not speaking English or Spanish. Clinicians complete a standardised history and physical examination of all eligible patients. Primary outcome is the presence of an EIA as determined by neuroimaging or clinical follow-up. We will use binary recursive partitioning and multiple regression analyses to create and internally validate the risk stratification model.
Ethics approval was obtained for all participating sites from the University of Utah single Institutional Review Board. A waiver of informed consent was granted for collection of ED data. Verbal consent is obtained for follow-up contact. Results will be disseminated through international conferences, peer-reviewed publications, and open-access materials.
头痛是儿童就诊于急诊科(ED)的常见主要主诉。约 0.5%-1%的儿童会出现紧急颅内异常(EIA),如脑肿瘤或中风。然而,超过三分之一的儿童在 ED 接受紧急神经影像学检查,导致大量儿童不必要地暴露于辐射之下。ED 中头痛儿童过度使用神经影像学检查是由临床医生对危及生命的 EIA 的担忧以及缺乏明确的临床特征来准确识别 EIA 儿童所驱动的。本研究旨在基于临床合理且可靠的变量,得出并内部验证一种准确识别头痛儿童 EIA 风险的分层模型。
前瞻性队列研究纳入了来自儿科急诊护理应用研究网络(PECARN)的 18 家 ED 中 28000 名头痛的儿童。纳入年龄为 2-17 岁、以头痛为主诉的儿童。排除具有明确的非颅内替代诊断、发热、神经影像学检查在过去 1 年内、神经或发育状况使病史或体格检查不可靠、格拉斯哥昏迷量表评分<14、中毒、已知妊娠、颅内手术史、脑结构异常、颅内异常或颅内高压的易患疾病、14 天内头部受伤或不会说英语或西班牙语的儿童。临床医生对所有符合条件的患者进行标准病史和体格检查。主要结局是通过神经影像学或临床随访确定的 EIA 存在。我们将使用二项递归分割和多元回归分析来创建和内部验证风险分层模型。
所有参与地点均获得了犹他大学单一机构审查委员会的伦理批准。ED 数据收集获得了知情同意豁免。随访联系时获得了口头同意。研究结果将通过国际会议、同行评议出版物和开放获取材料进行传播。