Stratification of risk for emergent intracranial abnormalities in children with headaches: a Pediatric Emergency Care Applied Research Network (PECARN) study protocol.
Stratification of risk for emergent intracranial abnormalities in children with headaches: a Pediatric Emergency Care Applied Research Network (PECARN) study protocol.
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DOI：
10.1136/bmjopen-2023-079040
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发表时间：
2023-11-22
期刊：
影响因子：
2.9
通讯作者：
Dayan, Peter S.
中科院分区：
文献类型：
--
作者：
Tsze, Daniel S.;Kuppermann, Nathan;Casper, T. Charles;Barney, Bradley J.;Richer, Lawrence P.;Liberman, Danica B.;Okada, Pamela J.;Morris, Claudia R.;Myers, Sage R.;Soung, Jane K.;Mistry, Rakesh D.;Babcock, Lynn;Spencer, Sandra P.;Johnson, Michael D.;Klein, Eileen J.;Quayle, Kimberly S.;Steele, Dale W.;Cruz, Andrea T.;Rogers, Alexander J.;Thomas, Danny G.;Grupp-Phelan, Jacqueline M.;Johnson, Tiffani J.;Dayan, Peter S.
头痛是儿童急诊常见的主诉。大约0.5%-1%的患者会出现颅内异常（EIA），如脑肿瘤或中风。然而，超过三分之一的儿童在艾德接受紧急神经影像检查，导致大量儿童不必要地暴露在辐射中。在艾德，临床医生对危及生命的EIA的担忧以及缺乏关于哪些临床特征可以准确识别EIA儿童的明确性，导致了神经影像学在头痛儿童中的过度使用。本研究的目的是推导和内部验证一个分层模型，该模型基于临床合理和可靠的变量准确识别头痛儿童的EIA风险。在儿科急诊应用研究网络（PECARN）中对28000名头痛儿童进行的前瞻性队列研究，这些儿童在18个急诊室中就诊。我们纳入了以头痛为主诉的2-17岁儿童。我们排除了以下儿童：明确的非颅内替代诊断、发热、前一年内的神经影像学检查、神经系统或发育状况，使得患者病史或体格检查可能不可靠、格拉斯哥昏迷量表评分<14、中毒、已知妊娠、颅内手术史、已知脑结构异常、易患颅内异常或颅内高压的既存状况，14天内头部受伤或不会说英语或西班牙语。临床医生完成所有合格患者的标准化病史和体格检查。主要结果是通过神经影像学或临床随访确定EIA的存在。我们将使用二元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。所有参与研究中心均获得了犹他州大学单一机构审查委员会的伦理批准。同意豁免采集艾德数据的知情同意书。后续联系需获得口头同意。将通过国际会议、同行评审出版物和开放获取材料传播成果。
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.
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影响因子：
1.4
作者：
Burton, LJ;Quinn, B;Pourani, M
通讯作者：
Pourani, M
影响因子：
5
作者：
Conicella, Elena;Raucci, Umberto;Valeriani, Massimiliano
通讯作者：
Valeriani, Massimiliano
影响因子：
2.1
作者：
Hsiao, Hsiang-Ju;Huang, Jing-Long;Wu, Chang-Teng
通讯作者：
Wu, Chang-Teng
DOI：
10.1001/archpedi.1982.03970380033008
发表时间：
1982-01-01
影响因子：
--
作者：
HONIG, PJ;CHARNEY, EB
通讯作者：
CHARNEY, EB
影响因子：
1.6
作者：
Kundra, Manu;Stankovic, Curt;Mahajan, Prashant
通讯作者：
Mahajan, Prashant