UNLABELLED: Four to ten percent of patients evaluated in emergency departments (ED) present with altered mental status (AMS). The prevalence of non-convulsive seizure (NCS) and other electroencephalographic (EEG) abnormalities in this population is unknown. OBJECTIVES: To identify the prevalence of NCS and other EEG abnormalities in ED patients with AMS. METHODS: A prospective observational study at 2 urban ED. Inclusion: patients ≥13 years old with AMS. Exclusion: An easily correctable cause of AMS (e.g. hypoglycemia). A 30-minute standard 21-electrode EEG was performed on each subject upon presentation. OUTCOME: prevalence of EEG abnormalities interpreted by a board-certified epileptologist. EEGs were later reviewed by 2 blinded epileptologists. Inter-rater agreement (IRA) of the blinded EEG interpretations is summarized with κ. A multiple logistic regression model was constructed to identify variables that could predict the outcome. RESULTS: Two hundred fifty-nine patients were enrolled (median age: 60, 54% female). Overall, 202/259 of EEGs were interpreted as abnormal (78%, 95% confidence interval [CI], 73-83%). The most common abnormality was background slowing (58%, 95% CI, 52-68%) indicating underlying encephalopathy. NCS (including non-convulsive status epilepticus [NCSE]) was detected in 5% (95% CI, 3-8%) of patients. The regression analysis predicting EEG abnormality showed a highly significant effect of age (P < .001, adjusted odds ratio 1.66 [95% CI, 1.36-2.02] per 10-year age increment). IRA for EEG interpretations was modest (κ: 0.45, 95% CI, 0.36-0.54). CONCLUSIONS: The prevalence of EEG abnormalities in ED patients with undifferentiated AMS is significant. ED physicians should consider EEG in the evaluation of patients with AMS and a high suspicion of NCS/NCSE.
UNLABELLED: Four to ten percent of patients evaluated in emergency departments (ED) present with altered mental status (AMS). The prevalence of non-convulsive seizure (NCS) and other electroencephalographic (EEG) abnormalities in this population is unknown. OBJECTIVES: To identify the prevalence of NCS and other EEG abnormalities in ED patients with AMS. METHODS: A prospective observational study at 2 urban ED. Inclusion: patients ≥13 years old with AMS. Exclusion: An easily correctable cause of AMS (e.g. hypoglycemia). A 30-minute standard 21-electrode EEG was performed on each subject upon presentation. OUTCOME: prevalence of EEG abnormalities interpreted by a board-certified epileptologist. EEGs were later reviewed by 2 blinded epileptologists. Inter-rater agreement (IRA) of the blinded EEG interpretations is summarized with κ. A multiple logistic regression model was constructed to identify variables that could predict the outcome. RESULTS: Two hundred fifty-nine patients were enrolled (median age: 60, 54% female). Overall, 202/259 of EEGs were interpreted as abnormal (78%, 95% confidence interval [CI], 73-83%). The most common abnormality was background slowing (58%, 95% CI, 52-68%) indicating underlying encephalopathy. NCS (including non-convulsive status epilepticus [NCSE]) was detected in 5% (95% CI, 3-8%) of patients. The regression analysis predicting EEG abnormality showed a highly significant effect of age (P < .001, adjusted odds ratio 1.66 [95% CI, 1.36-2.02] per 10-year age increment). IRA for EEG interpretations was modest (κ: 0.45, 95% CI, 0.36-0.54). CONCLUSIONS: The prevalence of EEG abnormalities in ED patients with undifferentiated AMS is significant. ED physicians should consider EEG in the evaluation of patients with AMS and a high suspicion of NCS/NCSE.
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