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Effect of microEEG on clinical management and outcomes of emergency department patients with altered mental status: a randomized controlled trial.

ABSTRACT: Altered mental status (AMS) is a common presentation in the emergency department (ED). A previous study revealed 78% electroencephalogram (EEG) abnormalities, including nonconvulsive seizure (NCS; 5%), in ED patients with AMS. The objective of this study was to assess the impact of EEG on clinical management and outcomes of ED patients with AMS.This was a randomized controlled trial at two urban teaching hospitals. Adult patients (?18 years old) with AMS were included. Excluded patients had immediately correctable AMS (e.g., hypoglycemia) or were admitted before enrollment. Patients were randomized to routine care (control) or routine care plus EEG (intervention). Research assistants used a scalp electrode set with a miniature, wireless EEG device (microEEG) to record standard 30-minute EEGs at presentation, and results were reported to the ED attending physician by an off-site epileptologist within 30 minutes. Primary outcomes included changes in ED management (differential diagnosis, diagnostic work-up, and treatment plan from enrollment to disposition) as determined by surveying the treating physicians. Secondary outcomes were length of ED and hospital stay, intensive care unit (ICU) requirement, and in-hospital mortality.A total of 149 patients were enrolled (76 control and 73 intervention). Patients in the two groups were comparable at baseline. EEG in the intervention group revealed abnormal findings in 93% (95% confidence interval [CI] = 85% to 97%), including NCS in 5% (95% CI = 2% to 13%). Using microEEG was associated with change in diagnostic work-up in 49% (95% CI = 38% to 60%) of cases and therapeutic plan in 42% (95% CI = 31% to 53%) of cases immediately after the release of EEG results. Changes in probabilities of differential diagnoses and the secondary outcomes were not statistically significant between the groups.An EEG can be obtained in the ED with minimal resources and can affect clinical management of AMS patients.

SUBMITTER: Zehtabchi S

PROVIDER: S-EPMC4047649 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

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Effect of microEEG on clinical management and outcomes of emergency department patients with altered mental status: a randomized controlled trial.

Zehtabchi Shahriar S Abdel Baki Samah G SG Omurtag Ahmet A Sinert Richard R Chari Geetha G Roodsari Gholamreza S GS Weedon Jeremy J Fenton André A AA Grant Arthur C AC

Academic emergency medicine : official journal of the Society for Academic Emergency Medicine 20140301 3

<h4>Objectives</h4>Altered mental status (AMS) is a common presentation in the emergency department (ED). A previous study revealed 78% electroencephalogram (EEG) abnormalities, including nonconvulsive seizure (NCS; 5%), in ED patients with AMS. The objective of this study was to assess the impact of EEG on clinical management and outcomes of ED patients with AMS.<h4>Methods</h4>This was a randomized controlled trial at two urban teaching hospitals. Adult patients (≥18 years old) with AMS were i ...[more]

PMID: 24628753

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Project description:BACKGROUND:Machine learning has been used extensively in clinical text classification tasks. Deep learning approaches using word embeddings have been recently gaining momentum in biomedical applications. In an effort to automate the identification of altered mental status (AMS) in emergency department provider notes for the purpose of decision support, we compare the performance of classic bag-of-words-based machine learning classifiers and novel deep learning approaches. METHODS:We used a case-control study design to extract an adequate number of clinical notes with AMS and non-AMS based on ICD codes. The notes were parsed to extract the history of present illness, which was used as the clinical text for the classifiers. The notes were manually labeled by clinicians. As a baseline for comparison, we tested several traditional bag-of-words based classifiers. We then tested several deep learning models using a convolutional neural network architecture with three different types of word embeddings, a pre-trained word2vec model and two models without pre-training but with different word embedding dimensions. RESULTS:We evaluated the models on 1130 labeled notes from the emergency department. The deep learning models had the best overall performance with an area under the ROC curve of 98.5% and an accuracy of 94.5%. Pre-training word embeddings on the unlabeled corpus reduced training iterations and had performance that was statistically no different than the other deep learning models. CONCLUSION:This supervised deep learning approach performs exceedingly well for the detection of AMS symptoms in clinical text in our environment. Further work is needed for the generalizability of these findings, including evaluation of these models in other types of clinical notes and other environments. The results seem promising for the ultimate use of these types of classifiers in combination with other information derived from the electronic health records as input for clinical decision support.

Project description:BackgroundPatients with mental illness have been shown to receive lower quality of care and experience worse cardiovascular (CV) outcomes compared to those without mental illness. This present study examined mental health-related disparities in CV outcomes after an Emergency Department (ED) visit for chest pain.MethodsThis retrospective cohort included adult Medicaid beneficiaries in Washington state discharged from the ED with a primary diagnosis of unspecified chest pain in 2010-2017. Outcomes for patients with any mental illness (any mental health diagnosis or mental-health specific service use within 1 year of an index ED visit) and serious mental illness (at least two claims (on different dates of service) within 1 year of an index ED visit with a diagnosis of schizophrenia, other psychotic disorder, or major mood disorder) were compared to those of patients without mental illness. Our outcomes of interest were the incidence of major adverse cardiac events (MACE) within 30 days and 6 months of discharge of their ED visit, defined as a composite of death, acute myocardial infarction (AMI), CV rehospitalization, or revascularization. Secondary outcomes included cardiovascular diagnostic testing (diagnostic angiography, stress testing, echocardiography, and coronary computed tomography (CT) angiography) rates within 30 days of ED discharge. Only treat-and-release visits were included for outcomes assessment. Hierarchical logistic random effects regression models assessed the association between mental illness and the outcomes of interest, controlling for age, gender, race, ethnicity, Elixhauser comorbidities, and health care use in the past year, as well as fixed year effects.ResultsThere were 98,812 treat-and-release ED visits in our dataset. At 30 days, enrollees with any mental illness had no differences in rates of MACE (AOR 0.96; 95% CI, 0.72-1.27) or any of the individual components. At 6 months, enrollees with any mental illness (AOR 1.86; 95% CI, 1.11-3.09) and serious mental illness (AOR 2.60; 95% CI 1.33-5.13) were significantly more likely to be hospitalized for a CV condition compared to those without mental illness. Individuals with any mental illness had higher rates of testing at 30 days (AOR 1.16; 95% CI 1.07-1.27).ConclusionPatients with mental illness have similar rates of MACE, but higher rates of certain CV outcomes, such as CV hospitalization and diagnostic testing, after an ED visit for chest pain.

Project description:BackgroundFrequent emergency department (ED) users account for a disproportionately high number of ED visits. Studies on case management (CM) interventions to reduce frequent ED use have shown mixed results, and few studies have been conducted within a universal health coverage system.ObjectiveTo determine whether a CM intervention-compared to standard emergency care-reduces ED attendance.DesignRandomized controlled trial.ParticipantsTwo hundred fifty frequent ED users (5 or more visits in the prior 12 months) who visited a public urban ED at the Lausanne University Hospital between May 2012 and July 2013 were allocated to either an intervention (n = 125) or control (n = 125) group, and monitored for 12 months.InterventionsAn individualized CM intervention consisting of concrete assistance in obtaining income entitlements, referral to primary or specialty medical care, access to mental health care or substance abuse treatment, and counseling on at-risk behaviors and health care utilization (in addition to standard care) at baseline and 1, 3, and 5 months.Main measuresWe used a generalized linear model for count data (negative binomial distribution) to compare the number of ED visits during the 12-month follow-up between CM and usual care, from an intention-to-treat perspective.Key resultsAt 12 months, there were 2.71 (±0.23) ED visits in the intervention group versus 3.35 (±0.32) visits among controls (ratio = 0.81, 95 % CI = 0.63; 1.02). In the multivariate model, the effect of the CM intervention on the number of ED visits approached statistical significance (b = -0.219, p = 0.075). The presence of poor social determinants of health was a significant predictor of ED use in the multivariate model (b = 0.280, p = 0.048).ConclusionsCM may reduce ED use by frequent users through an improved orientation to the health care system. Poor social determinants of health significantly increase use of the ED by frequent users.

Dataset Information

Effect of microEEG on clinical management and outcomes of emergency department patients with altered mental status: a randomized controlled trial.

Publications

Effect of microEEG on clinical management and outcomes of emergency department patients with altered mental status: a randomized controlled trial.

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