Evidence map›Paper›PMID 35806880›Full record

ArticleJournal of clinical medicine2022

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

Sung-Eun Lee, Seungyon Koh, Ju-Min Park, Tae-Joon Kim, Hee-Won Yang, Ji-Hyun Park, Han-Bit Shin, Bumhee Park, Byung-Gon Kim, Kyoon Huh and 1 more

Abstract read
In one paragraph

Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Sung-Eun LeeDepartment of Emergency Medicine, School of Medicine, Ajou University, Suwon 16499, Korea.ORCID 0000-0002-6402-7259
Seungyon KohDepartment of Neurology, School of Medicine, Ajou University, Suwon 16499, Korea.ORCID 0000-0002-5547-6378
Ju-Min ParkDepartment of Emergency Medicine, School of Medicine, Ajou University, Suwon 16499, Korea.
Tae-Joon KimDepartment of Neurology, School of Medicine, Ajou University, Suwon 16499, Korea.ORCID 0000-0001-8451-6634
Hee-Won YangDepartment of Emergency Medicine, School of Medicine, Ajou University, Suwon 16499, Korea.ORCID 0000-0002-7592-2240
Ji-Hyun ParkOffice of Biostatics, Ajou Research Institute for Innovation Medicine, Ajou University Medical Center, Suwon 16499, Korea.
Han-Bit ShinOffice of Biostatics, Ajou Research Institute for Innovation Medicine, Ajou University Medical Center, Suwon 16499, Korea.
Bumhee ParkOffice of Biostatics, Ajou Research Institute for Innovation Medicine, Ajou University Medical Center, Suwon 16499, Korea.
Byung-Gon KimDepartment of Neurology, School of Medicine, Ajou University, Suwon 16499, Korea.
Kyoon HuhDepartment of Neurology, School of Medicine, Ajou University, Suwon 16499, Korea.
Jun-Young ChoiDepartment of Neurology, School of Medicine, Ajou University, Suwon 16499, Korea.ORCID 0000-0002-6031-3549

Funding

Korea Health Industry Development Institute HR21C1003National Research Foundation of Korea NRF-2018M3A9E8023853National Research Foundation of Korea NRF-2019R1A5A2026045National Research Foundation of Korea NRF-2021R1F1A1061819
6 · The paper itself

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.

Indexed as

emergency departmentprediction equationrecurrenceseizuretriage

Identifiers

PMID35806880
PMCPMC9267812

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.