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Status epilepticus severity score as a predictor for the length of stay at hospital for acute-phase treatment in convulsive status epilepticus
Affiliation:1. Department of Thoracic and Cardiovascular Surgery, Cleveland Clinic, Cleveland, Ohio;2. Maine Medical Partners MaineHealth Cardiology, Scarborough, Maine;3. Department of Cardiovascular Medicine, Cleveland Clinic, Cleveland, Ohio;1. Department of Neurology, Washington University in St. Louis School of Medicine, United States;2. Department of Neurology, Ohio State University College of Medicine, United States;3. Department of Neurosurgery, UT Health San Antonio Lozano School of Medicine, United States;1. Princess Alexandra Hospital, Brisbane, Qld, Australia;2. University of Queensland2, Brisbane, Qld, Australia;1. Department of Neurology, Oslo University Hospital, Oslo, Norway;2. Department of Neurosurgery, Oslo University Hospital, Oslo, Norway;3. Department of Neurology, Østfold Hospital Trust, Norway;4. Faculty of Medicine, University of Oslo, Oslo, Norway
Abstract:To date, hospital length of stay (LOS) determinants for convulsive status epilepticus’s (CSE) acute-phase treatment have not been sufficiently investigated, as opposed to those for status epilepticus’s (SE) outcome predictors, such as status epilepticus severity score (STESS). Here, we aimed at assessing the significance of STESS in the LOS in patients with CSE. We retrospectively reviewed consecutive adult patients with CSE who were transported to the emergency department of our urban tertiary care hospital in Tokyo, Japan. The study period was from August 2010 to September 2015. The primary endpoint was the LOS of patients with CSE who were directly discharged after acute-phase treatment, and survival analysis for LOS until discharge was conducted. As a result, among 132 eligible patients with CSE admitted to our hospital, 96 (72.7%) were directly discharged with a median LOS of 10 days (IQR: 4–19 days). CSE patients with severe seizures, represented by higher STESS (≥3), had a significantly longer LOS after adjustments with multiple covariates (p = 0.016, in restricted mean survival time analysis). Additionally, prediction for the binomial longer/shorter LOS achieved better performance when STESS was incorporated into the prediction model. Our findings indicate that STESS can also be used as a rough predictor of longer LOS at index admission of patients with CSE.
Keywords:Convulsive seizure  Length of stay  Machine learning  Status epilepticus  Status epilepticus severity score
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