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ES-RED(急诊科早期癫痫复发)计算器:一种用于癫痫患者的分诊工具。

ES-RED (Early Seizure Recurrence in the Emergency Department) Calculator: A Triage Tool for Seizure Patients.

作者信息

Lee Sung-Eun, Koh Seungyon, Park Ju-Min, Kim Tae-Joon, Yang Hee-Won, Park Ji-Hyun, Shin Han-Bit, Park Bumhee, Kim Byung-Gon, Huh Kyoon, Choi Jun-Young

机构信息

Department of Emergency Medicine, School of Medicine, Ajou University, Suwon 16499, Korea.

Department of Neurology, School of Medicine, Ajou University, Suwon 16499, Korea.

出版信息

J Clin Med. 2022 Jun 22;11(13):3598. doi: 10.3390/jcm11133598.

Abstract

Seizure is a common neurological presentation in patients visiting the emergency department (ED) that requires time for evaluation and observation. Timely decision and disposition standards for seizure patients need to be established to prevent overcrowding in the ED and achieve patients' safety. Here, we conducted a retrospective cohort study to predict early seizure recurrence in the ED (ES-RED). We randomly assigned 688 patients to the derivation and validation cohorts (2:1 ratio). Prediction equations extracted routine clinical and laboratory information from EDs using logistic regression (Model 1) and machine learning (Model 2) methods. The prediction equations showed good predictive performance, the area under the receiver operating characteristics curve showing 0.808 in Model 1 [95% confidential interval (CI): 0.761-0.853] and 0.805 in Model 2 [95% CI: 0.747-0.857] in the derivation cohort. In the external validation, the models showed strong prediction performance of 0.739 [95% CI: 0.640-0.824] in Model 1 and 0.738 [95% CI: 0.645-0.819] in Model 2. Intriguingly, the lowest quartile group showed no ES-RED after 6 h. The ES-RED calculator, our proposed prediction equation, would provide strong evidence for safe and appropriate disposition of adult resolved seizure patients from EDs, reducing overcrowding and delays and improving patient safety.

摘要

癫痫发作是急诊科就诊患者常见的神经系统症状,需要时间进行评估和观察。需要制定癫痫患者的及时决策和处置标准,以防止急诊科过度拥挤并确保患者安全。在此,我们进行了一项回顾性队列研究,以预测急诊科早期癫痫复发(ES-RED)。我们将688例患者随机分配到推导队列和验证队列(比例为2:1)。预测方程使用逻辑回归(模型1)和机器学习(模型2)方法从急诊科提取常规临床和实验室信息。预测方程显示出良好的预测性能,在推导队列中,模型1的受试者操作特征曲线下面积为0.808 [95%置信区间(CI):0.761-0.853],模型2为0.805 [95%CI:0.747-0.857]。在外部验证中,模型1的预测性能为0.739 [95%CI:0.640-0.824],模型2为0.738 [95%CI:0.645-0.819]。有趣的是,最低四分位数组在6小时后未出现ES-RED。我们提出的预测方程ES-RED计算器,将为急诊科成年癫痫已缓解患者的安全和适当处置提供有力证据,减少过度拥挤和延误,提高患者安全性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f6/9267812/18d6531649d2/jcm-11-03598-g001.jpg

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