Nascimento Fábio A, Gao Hong, Katyal Roohi, Matthews Rebecca, Yap Samantha V, Rampp Stefan, Tatum William O, Strowd Roy E, Beniczky Sándor
From the Division of Epilepsy (F.A.N.), Department of Neurology, Washington University School of Medicine, St. Louis, MO; Department of Neurology (F.A.N., S.V.Y.), Massachusetts General Hospital, Harvard Medical School, Boston; Department of Internal Medicine (H.G.), Wake Forest University School of Medicine, Winston-Salem, NC; Division of Epilepsy (R.K.), Department of Neurology, Louisiana State University Health Shreveport; Department of Neurology (R.M.), Emory University School of Medicine, Atlanta, GA; Department of Neurosurgery (S.R.), University Hospital Erlangen; Department of Neurosurgery (S.R.), University Hospital Halle (Saale), Germany; Department of Neurology (W.O.T.), Mayo Clinic, Jacksonville, FL; Department of Neurology (R.E.S.), Wake Forest University School of Medicine, Winston-Salem, NC; Department of Clinical Neurophysiology (S.B.), Danish Epilepsy Center, Dianalund and Aarhus University Hospital; and Department of Clinical Medicine (S.B.), Aarhus University, Denmark.
Neurol Educ. 2023 Oct 16;2(4):e200094. doi: 10.1212/NE9.0000000000200094. eCollection 2023 Dec 22.
We recently published expert consensus-based curricular objectives for routine EEG (rEEG) interpretation for adult and child neurology residents. In this study, we used this curriculum framework to develop and validate an online, competency-based, formative and summative rEEG examination for neurology residents.
We developed an online rEEG examination consisting of a brief survey and 30 multiple-choice questions covering EEG learning objectives for neurology residents in 4 domains: normal, abnormal, normal variants, and artifacts. Each question contained a deidentified EEG image, displayed in 2 montages (bipolar and average), reviewed and optimized by the authors to address the learning objectives. Respondents reported their level of confidence (LOC, 5-point Likert scale) with identifying 4 categories of EEG findings independently: states of wakefulness/sleep, sleep structures, normal variants, and artifacts. Accuracy and item discrimination were calculated for each question and LOC for each category. The test was disseminated by the International League Against Epilepsy and shared on social media.
Of 2,080 responses, 922 were complete. Respondents comprised clinical neurophysiologists/experts (n = 41), EEG/epilepsy clinical fellows (n = 211), EEG technologists (n = 128), attending neurologists (n = 111), adult neurology residents (n = 227), child neurology residents (n = 108), medical students (n = 24), attending non-neurologists (n = 18), and others (n = 54). Mean overall scores (95% CI) were 82% (77-86) (clinical neurophysiologists), 81% (79-83) (clinical fellows), and 72% (70-73) (adult and child neurology residents). Experts were more confident than clinical fellows in all categories but sleep structures. Experts and clinical fellows were more confident than residents in all 4 categories. Among residents, accuracy and LOC increased as a function of prior EEG weeks of training. Accuracy improved from 67% (baseline/no prior EEG training) to 77% (>12 prior EEG weeks). More than 8 weeks of EEG training was needed to reach accuracy comparable with clinical neurophysiologists on this rEEG examination. Increase in LOC was slower and less robust than increase in accuracy. All but 3 questions had a high discrimination index (>0.25).
This online, competency-based rEEG examination, mapped to a published EEG curriculum, has excellent psychometrics and differentiates experienced EEG readers from adult and child neurology residents. This online tool has the potential to improve resident EEG education worldwide.
我们最近发布了基于专家共识的成人及儿童神经科住院医师常规脑电图（rEEG）解读课程目标。在本研究中，我们使用该课程框架为神经科住院医师开发并验证了一项基于能力的在线rEEG形成性和总结性考试。
我们开发了一项在线rEEG考试，包括一份简短调查问卷和30道多项选择题，涵盖神经科住院医师脑电图学习目标的4个领域：正常、异常、正常变异和伪迹。每个问题都包含一张去识别化的脑电图图像，以两种导联方式（双极导联和平均导联）显示，由作者进行审核和优化以实现学习目标。受访者独立报告他们识别4类脑电图结果的信心水平（LOC，5级李克特量表）：清醒/睡眠状态、睡眠结构、正常变异和伪迹。计算每个问题的准确率和项目区分度以及每个类别的LOC。该测试由国际抗癫痫联盟发布并在社交媒体上分享。
在2080份回复中，922份完整。受访者包括临床神经生理学家/专家（n = 41）、脑电图/癫痫临床研究员（n = 211）、脑电图技术人员（n = 128）、神经科主治医师（n = 111）、成人神经科住院医师（n = 227）、儿童神经科住院医师（n = 108）、医学生（n = 24）、非神经科主治医师（n = 18）以及其他人员（n = 54）。总体平均得分（95% CI）分别为82%（77 - 86）（临床神经生理学家）、81%（79 - 83）（临床研究员）以及72%（70 - 73）（成人和儿童神经科住院医师）。除睡眠结构外，专家在所有类别中的信心都高于临床研究员。专家和临床研究员在所有4个类别中的信心都高于住院医师。在住院医师中，准确率和LOC随着之前脑电图培训周数的增加而提高。准确率从67%（基线/无先前脑电图培训）提高到77%（超过12周先前脑电图培训）。在这项rEEG考试中，需要超过8周的脑电图培训才能达到与临床神经生理学家相当的准确率。LOC的增加比准确率的增加更缓慢且不那么显著。除3个问题外，所有问题的区分指数都很高（>0.25）。
这项基于能力的在线rEEG考试与已发布的脑电图课程相匹配，具有出色的心理测量学特性，能够区分经验丰富的脑电图阅读者与成人及儿童神经科住院医师。这个在线工具有可能改善全球住院医师的脑电图教育。
