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ES-RED (Early Seizure Recurrence in the Emergency Department) Calculator: A Triage Tool for Seizure Patients
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. He...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9267812/ https://www.ncbi.nlm.nih.gov/pubmed/35806880 http://dx.doi.org/10.3390/jcm11133598 |
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author | 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 |
author_facet | 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 |
author_sort | Lee, Sung-Eun |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9267812 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92678122022-07-09 ES-RED (Early Seizure Recurrence in the Emergency Department) Calculator: A Triage Tool for Seizure Patients 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 J Clin Med Article 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. MDPI 2022-06-22 /pmc/articles/PMC9267812/ /pubmed/35806880 http://dx.doi.org/10.3390/jcm11133598 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article 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 ES-RED (Early Seizure Recurrence in the Emergency Department) Calculator: A Triage Tool for Seizure Patients |
title | ES-RED (Early Seizure Recurrence in the Emergency Department) Calculator: A Triage Tool for Seizure Patients |
title_full | ES-RED (Early Seizure Recurrence in the Emergency Department) Calculator: A Triage Tool for Seizure Patients |
title_fullStr | ES-RED (Early Seizure Recurrence in the Emergency Department) Calculator: A Triage Tool for Seizure Patients |
title_full_unstemmed | ES-RED (Early Seizure Recurrence in the Emergency Department) Calculator: A Triage Tool for Seizure Patients |
title_short | ES-RED (Early Seizure Recurrence in the Emergency Department) Calculator: A Triage Tool for Seizure Patients |
title_sort | es-red (early seizure recurrence in the emergency department) calculator: a triage tool for seizure patients |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9267812/ https://www.ncbi.nlm.nih.gov/pubmed/35806880 http://dx.doi.org/10.3390/jcm11133598 |
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