Impact of the COVID-19 Pandemic on Continuous EEG Utilization.

PURPOSE: The coronavirus disease 2019 (COVID-19) has significantly impacted healthcare delivery and utilization. The aim of this article was to assess the impact of the COVID-19 pandemic on in-hospital continuous electroencephalography (cEEG) utilization and identify areas for process improvement. METHODS: A 38-question web-based survey was distributed to site principal investigators of the Critical Care EEG Monitoring Research Consortium, and institutional contacts for the Neurodiagnostic Credentialing and Accreditation Board. The survey addressed the following aspects of cEEG utilization: (1) general center characteristics, (2) cEEG utilization and review, (3) staffing and workflow, and (4) health impact on EEG technologists. RESULTS: The survey was open from June 12, 2020 to June 30, 2020 and distributed to 174 centers with 79 responses (45.4%). Forty centers were located in COVID-19 hotspots. Fifty-seven centers (72.1%) reported cEEG volume reduction. Centers in the Northeast were most likely to report cEEG volume reduction (odds ratio [OR] 7.19 [1.53-33.83]; P = 0.012). Additionally, centers reporting decrease in outside hospital transfers reported cEEG volume reduction; OR 21.67 [4.57-102.81]; P ≤ 0.0001. Twenty-six centers (32.91%) reported reduction in EEG technologist coverage. Eighteen centers had personal protective equipment shortages for EEG technologists. Technologists at these centers were more likely to quarantine for suspected or confirmed COVID-19; OR 3.14 [1.01-9.63]; P = 0.058. CONCLUSIONS: There has been a widespread reduction in cEEG volume during the pandemic. Given the anticipated duration of the pandemic and the importance of cEEG in managing hospitalized patients, methods to optimize use need to be prioritized to provide optimal care. Because the survey provides a cross-sectional assessment, follow-up studies can determine the long-term impact of the pandemic on cEEG utilization.

Classification of neurologic outcomes from medical notes using natural language processing.

Neurologic disability level at hospital discharge is an important outcome in many clinical research studies. Outside of clinical trials, neurologic outcomes must typically be extracted by labor intensive manual review of clinical notes in the electronic health record (EHR). To overcome this challenge, we set out to develop a natural language processing (NLP) approach that automatically reads clinical notes to determine neurologic outcomes, to make it possible to conduct larger scale neurologic outcomes studies. We obtained 7314 notes from 3632 patients hospitalized at two large Boston hospitals between January 2012 and June 2020, including discharge summaries (3485), occupational therapy (1472) and physical therapy (2357) notes. Fourteen clinical experts reviewed notes to assign scores on the Glasgow Outcome Scale (GOS) with 4 classes, namely 'good recovery', 'moderate disability', 'severe disability', and 'death' and on the Modified Rankin Scale (mRS), with 7 classes, namely 'no symptoms', 'no significant disability', 'slight disability', 'moderate disability', 'moderately severe disability', 'severe disability', and 'death'. For 428 patients' notes, 2 experts scored the cases generating interrater reliability estimates for GOS and mRS. After preprocessing and extracting features from the notes, we trained a multiclass logistic regression model using LASSO regularization and 5-fold cross validation for hyperparameter tuning. The model performed well on the test set, achieving a micro average area under the receiver operating characteristic and F-score of 0.94 (95% CI 0.93-0.95) and 0.77 (0.75-0.80) for GOS, and 0.90 (0.89-0.91) and 0.59 (0.57-0.62) for mRS, respectively. Our work demonstrates that an NLP algorithm can accurately assign neurologic outcomes based on free text clinical notes. This algorithm increases the scale of research on neurological outcomes that is possible with EHR data.

Telemedicine Can Support Measurable and High-Quality Epilepsy Care During the COVID-19 Pandemic.

Routine outpatient epilepsy care has shifted from in-person to telemedicine visits in response to safety concerns posed by the coronavirus disease 2019 (COVID-19) pandemic. But whether telemedicine can support and maintain standardized documentation of high-quality epilepsy care remains unknown. In response, the authors conducted a quality improvement study at a level 4 epilepsy center between January 20, 2019, and May 31, 2020. Weekly average completion proportion of standardized documentation used by a team of neurologists for adult patients for the diagnosis of epilepsy, seizure classification, and frequency were analyzed. By December 15, 2019, a 94% average weekly completion proportion of standardized epilepsy care documentation was achieved that was maintained through May 31, 2020. Moreover, during the period of predominately telemedicine encounters in response to the pandemic, the completion proportion was 90%. This study indicates that high completion of standardized documentation of seizure-related information can be sustained during telemedicine appointments for routine outpatient epilepsy care at a level 4 epilepsy center.

Clinical Electroencephalography Findings and Considerations in Hospitalized Patients With Coronavirus SARS-CoV-2.

BACKGROUND AND PURPOSE: Reports have suggested that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes neurologic manifestations including encephalopathy and seizures. However, there has been relatively limited electrophysiology data to contextualize these specific concerns and to understand their associated clinical factors. Our objective was to identify EEG abnormalities present in patients with SARS-CoV-2, and to determine whether they reflect new or preexisting brain pathology. METHODS: We studied a consecutive series of hospitalized patients with SARS-CoV-2 who received an EEG, obtained using tailored safety protocols. Data from EEG reports and clinical records were analyzed to identify EEG abnormalities and possible clinical associations, including neurologic symptoms, new or preexisting brain pathology, and sedation practices. RESULTS: We identified 37 patients with SARS-CoV-2 who underwent EEG, of whom 14 had epileptiform findings (38%). Patients with epileptiform findings were more likely to have preexisting brain pathology (6/14, 43%) than patients without epileptiform findings (2/23, 9%; p = 0.042). There were no clear differences in rates of acute brain pathology. One case of nonconvulsive status epilepticus was captured, but was not clearly a direct consequence of SARS-CoV-2. Abnormalities of background rhythms were common, as may be seen in systemic illness, and in part associated with recent sedation (p = 0.022). CONCLUSIONS: Epileptiform abnormalities were common in patients with SARS-CoV-2 referred for EEG, but particularly in the context of preexisting brain pathology and sedation. These findings suggest that neurologic manifestations during SARS-CoV-2 infection may not solely relate to the infection itself, but rather may also reflect patients' broader, preexisting neurologic vulnerabilities.