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.

Development and Evaluation of a Natural Language Processing Annotation Tool to Facilitate Phenotyping of Cognitive Status in Electronic Health Records: Diagnostic Study.

BACKGROUND: Electronic health records (EHRs) with large sample sizes and rich information offer great potential for dementia research, but current methods of phenotyping cognitive status are not scalable. OBJECTIVE: The aim of this study was to evaluate whether natural language processing (NLP)-powered semiautomated annotation can improve the speed and interrater reliability of chart reviews for phenotyping cognitive status. METHODS: In this diagnostic study, we developed and evaluated a semiautomated NLP-powered annotation tool (NAT) to facilitate phenotyping of cognitive status. Clinical experts adjudicated the cognitive status of 627 patients at Mass General Brigham (MGB) health care, using NAT or traditional chart reviews. Patient charts contained EHR data from two data sets: (1) records from January 1, 2017, to December 31, 2018, for 100 Medicare beneficiaries from the MGB Accountable Care Organization and (2) records from 2 years prior to COVID-19 diagnosis to the date of COVID-19 diagnosis for 527 MGB patients. All EHR data from the relevant period were extracted; diagnosis codes, medications, and laboratory test values were processed and summarized; clinical notes were processed through an NLP pipeline; and a web tool was developed to present an integrated view of all data. Cognitive status was rated as cognitively normal, cognitively impaired, or undetermined. Assessment time and interrater agreement of NAT compared to manual chart reviews for cognitive status phenotyping was evaluated. RESULTS: NAT adjudication provided higher interrater agreement (Cohen κ=0.89 vs κ=0.80) and significant speed up (time difference mean 1.4, SD 1.3 minutes; P<.001; ratio median 2.2, min-max 0.4-20) over manual chart reviews. There was moderate agreement with manual chart reviews (Cohen κ=0.67). In the cases that exhibited disagreement with manual chart reviews, NAT adjudication was able to produce assessments that had broader clinical consensus due to its integrated view of highlighted relevant information and semiautomated NLP features. CONCLUSIONS: NAT adjudication improves the speed and interrater reliability for phenotyping cognitive status compared to manual chart reviews. This study underscores the potential of an NLP-based clinically adjudicated method to build large-scale dementia research cohorts from EHRs.

Identifying Patients With Delirium Based on Unstructured Clinical Notes: Observational Study.

BACKGROUND: Delirium in hospitalized patients is a syndrome of acute brain dysfunction. Diagnostic (International Classification of Diseases [ICD]) codes are often used in studies using electronic health records (EHRs), but they are inaccurate. OBJECTIVE: We sought to develop a more accurate method using natural language processing (NLP) to detect delirium episodes on the basis of unstructured clinical notes. METHODS: We collected 1.5 million notes from >10,000 patients from among 9 hospitals. Seven experts iteratively labeled 200,471 sentences. Using these, we trained three NLP classifiers: Support Vector Machine, Recurrent Neural Networks, and Transformer. Testing was performed using an external data set. We also evaluated associations with delirium billing (ICD) codes, medications, orders for restraints and sitters, direct assessments (Confusion Assessment Method [CAM] scores), and in-hospital mortality. F1 scores, confusion matrices, and areas under the receiver operating characteristic curve (AUCs) were used to compare NLP models. We used the φ coefficient to measure associations with other delirium indicators. RESULTS: The transformer NLP performed best on the following parameters: micro F1=0.978, macro F1=0.918, positive AUC=0.984, and negative AUC=0.992. NLP detections exhibited higher correlations (φ) than ICD codes with deliriogenic medications (0.194 vs 0.073 for ICD codes), restraints and sitter orders (0.358 vs 0.177), mortality (0.216 vs 0.000), and CAM scores (0.256 vs -0.028). CONCLUSIONS: Clinical notes are an attractive alternative to ICD codes for EHR delirium studies but require automated methods. Our NLP model detects delirium with high accuracy, similar to manual chart review. Our NLP approach can provide more accurate determination of delirium for large-scale EHR-based studies regarding delirium, quality improvement, and clinical trails.

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.

Electroencephalographic Abnormalities are Common in COVID-19 and are Associated with Outcomes.

OBJECTIVE: The aim was to determine the prevalence and risk factors for electrographic seizures and other electroencephalographic (EEG) patterns in patients with Coronavirus disease 2019 (COVID-19) undergoing clinically indicated continuous electroencephalogram (cEEG) monitoring and to assess whether EEG findings are associated with outcomes. METHODS: We identified 197 patients with COVID-19 referred for cEEG at 9 participating centers. Medical records and EEG reports were reviewed retrospectively to determine the incidence of and clinical risk factors for seizures and other epileptiform patterns. Multivariate Cox proportional hazards analysis assessed the relationship between EEG patterns and clinical outcomes. RESULTS: Electrographic seizures were detected in 19 (9.6%) patients, including nonconvulsive status epilepticus (NCSE) in 11 (5.6%). Epileptiform abnormalities (either ictal or interictal) were present in 96 (48.7%). Preceding clinical seizures during hospitalization were associated with both electrographic seizures (36.4% in those with vs 8.1% in those without prior clinical seizures, odds ratio [OR] 6.51, p = 0.01) and NCSE (27.3% vs 4.3%, OR 8.34, p = 0.01). A pre-existing intracranial lesion on neuroimaging was associated with NCSE (14.3% vs 3.7%; OR 4.33, p = 0.02). In multivariate analysis of outcomes, electrographic seizures were an independent predictor of in-hospital mortality (hazard ratio [HR] 4.07 [1.44-11.51], p < 0.01). In competing risks analysis, hospital length of stay increased in the presence of NCSE (30 day proportion discharged with vs without NCSE: HR 0.21 [0.03-0.33] vs 0.43 [0.36-0.49]). INTERPRETATION: This multicenter retrospective cohort study demonstrates that seizures and other epileptiform abnormalities are common in patients with COVID-19 undergoing clinically indicated cEEG and are associated with adverse clinical outcomes. ANN NEUROL 2021;89:872-883.

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.