A Modified Delphi Consensus Approach to Define Entrustable Professional Activities for Neurocritical Care Advanced Practice Providers.

OBJECTIVES: To define consensus entrustable professional activities (EPAs) for neurocritical care (NCC) advanced practice providers (APPs), establish validity evidence for the EPAs, and evaluate factors that inform entrustment expectations of NCC APP supervisors. DESIGN: A three-round modified Delphi consensus process followed by application of the EQual rubric and assessment of generalizability by clinicians not affiliated with academic medical centers. SETTING: Electronic surveys. SUBJECTS: NCC APPs ( n = 18) and physicians ( n = 12) in the United States with experience in education scholarship or APP program leadership. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The steering committee generated an initial list of 61 possible EPAs. The panel proposed 30 additional EPAs. A total of 47 unique nested EPAs were retained by consensus opinion. The steering committee defined six core EPAs addressing medical knowledge, procedural competencies, and communication proficiency which encompassed the nested EPAs. All core EPAs were retained and subsequently met the previously described cut score for quality and structure using the EQual rubric. Most clinicians who were not affiliated with academic medical centers rated each of the six core EPAs as very important or mandatory. Entrustment expectations did not vary by prespecified groups. CONCLUSIONS: Expert consensus was used to create EPAs for NCC APPs that reached a predefined quality standard and were important to most clinicians in different practice settings. We did not identify variables that significantly predicted entrustment expectations. These EPAs may aid in curricular design for an EPA-based assessment of new NCC APPs and may inform the development of EPAs for APPs in other critical care subspecialties.

Commentary on stimulus-induced arousal with transient electroencephalographic improvement distinguishes nonictal from ictal generalized periodic discharges.

Here we critique recent arguments proposing to distinguish ictal from non-ictal generalized periodic discharges (GPDs) based on etiology and stimulation response, arguing that these are unreliable. We advocate for an empirical approach to GPDs: describe objectively, interpret through medication trials, and base further treatment on response. We call for evidence-based approaches considering meaningful clinical outcomes.

Immediate and long-term management practices of acute symptomatic seizures and epileptiform abnormalities: A cross-sectional international survey.

OBJECTIVES: Acute symptomatic seizures (ASyS) and epileptiform abnormalities (EAs) on electroencephalography (EEG) are commonly encountered following acute brain injury. Their immediate and long-term management remains poorly investigated. We conducted an international survey to understand their current management. METHODS: The cross-sectional web-based survey of 21 fixed-response questions was based on a common clinical encounter: convulsive or suspected ASyS following an acute brain injury. Respondents selected the option that best matched their real-world practice. Respondents completing the survey were compared with those who accessed but did not complete it. RESULTS: A total of 783 individuals (44 countries) accessed the survey; 502 completed it. Almost everyone used anti-seizure medications (ASMs) for secondary prophylaxis after convulsive or electrographic ASyS (95.4% and 97.2%, respectively). ASM dose escalation after convulsive ASyS depends on continuous EEG (cEEG) findings: most often increased after electrographic seizures (78% of respondents), followed by lateralized periodic discharges (LPDs; 41%) and sporadic epileptiform discharges (sEDs; 17.5%). If cEEG is unrevealing, one in five respondents discontinue ASMs after a week. In the absence of convulsive and electrographic ASyS, a large proportion of respondents start ASMs due to LPD (66.7%) and sED (44%) on cEEG. At hospital discharge, most respondents (85%) continue ASM without dose change. The recommended duration of outpatient ASM use is as follows: 1-3 months (36%), 3-6 months (30%), 6-12 months (13%), >12 months (11%). Nearly one-third of respondents utilized ancillary testing before outpatient ASM taper, most commonly (79%) a <2 h EEG. Approximately half of respondents had driving restrictions recommended for 6 months after discharge. SIGNIFICANCE: ASM use for secondary prophylaxis after convulsive and electrographic ASyS is a universal practice and is continued upon discharge. Outpatient care, particularly the ASM duration, varies significantly. Wide practice heterogeneity in managing acute EAs reflects uncertainty about their significance and management. These results highlight the need for a structured outpatient follow-up and optimized care pathway for patients with ASyS.

Comparative analysis of patients with new onset refractory status epilepticus preceded by fever (febrile infection-related epilepsy syndrome) versus without prior fever: An interim analysis.

Febrile infection-related epilepsy syndrome (FIRES) is a subset of new onset refractory status epilepticus (NORSE) that involves a febrile infection prior to the onset of the refractory status epilepticus. It is unclear whether FIRES and non-FIRES NORSE are distinct conditions. Here, we compare 34 patients with FIRES to 30 patients with non-FIRES NORSE for demographics, clinical features, neuroimaging, and outcomes. Because patients with FIRES were younger than patients with non-FIRES NORSE (median = 28 vs. 48 years old, p = .048) and more likely cryptogenic (odds ratio = 6.89), we next ran a regression analysis using age or etiology as a covariate. Respiratory and gastrointestinal prodromes occurred more frequently in FIRES patients, but no difference was found for non-infection-related prodromes. Status epilepticus subtype, cerebrospinal fluid (CSF) and magnetic resonance imaging findings, and outcomes were similar. However, FIRES cases were more frequently cryptogenic; had higher CSF interleukin 6, CSF macrophage inflammatory protein-1 alpha (MIP-1a), and serum chemokine ligand 2 (CCL2) levels; and received more antiseizure medications and immunotherapy. After controlling for age or etiology, no differences were observed in presenting symptoms and signs or inflammatory biomarkers, suggesting that FIRES and non-FIRES NORSE are very similar conditions.

Trends in management of patients with new-onset refractory status epilepticus (NORSE) from 2016 to 2023: An interim analysis.

In response to the evolving treatment landscape for new-onset refractory status epilepticus (NORSE) and the publication of consensus recommendations in 2022, we conducted a comparative analysis of NORSE management over time. Seventy-seven patients were enrolled by 32 centers, from July 2016 to August 2023, in the NORSE/FIRES biorepository at Yale. Immunotherapy was administered to 88% of patients after a median of 3 days, with 52% receiving second-line immunotherapy after a median of 12 days (anakinra 29%, rituximab 25%, and tocilizumab 19%). There was an increase in the use of second-line immunotherapies (odds ratio [OR] = 1.4, 95% CI = 1.1-1.8) and ketogenic diet (OR = 1.8, 95% CI = 1.3-2.6) over time. Specifically, patients from 2022 to 2023 more frequently received second-line immunotherapy (69% vs 40%; OR = 3.3; 95% CI = 1.3-8.9)-particularly anakinra (50% vs 13%; OR = 6.5; 95% CI = 2.3-21.0), and the ketogenic diet (OR = 6.8; 95% CI = 2.5-20.1)-than those before 2022. Among the 27 patients who received anakinra and/or tocilizumab, earlier administration after status epilepticus onset correlated with a shorter duration of status epilepticus (ρ = .519, p = .005). Our findings indicate an evolution in NORSE management, emphasizing the increasing use of second-line immunotherapies and the ketogenic diet. Future research will clarify the impact of these treatments and their timing on patient outcomes.

Guidelines for Seizure Prophylaxis in Adults Hospitalized with Moderate-Severe Traumatic Brain Injury: A Clinical Practice Guideline for Health Care Professionals from the Neurocritical Care Society.

BACKGROUND: There is practice heterogeneity in the use, type, and duration of prophylactic antiseizure medications (ASMs) in patients with moderate-severe traumatic brain injury (TBI). METHODS: We conducted a systematic review and meta-analysis of articles assessing ASM prophylaxis in adults with moderate-severe TBI (acute radiographic findings and requiring hospitalization). The population, intervention, comparator, and outcome (PICO) questions were as follows: (1) Should ASM versus no ASM be used in patients with moderate-severe TBI and no history of clinical or electrographic seizures? (2) If an ASM is used, should levetiracetam (LEV) or phenytoin/fosphenytoin (PHT/fPHT) be preferentially used? (3) If an ASM is used, should a long versus short (> 7 vs. ≤ 7 days) duration of prophylaxis be used? The main outcomes were early seizure, late seizure, adverse events, mortality, and functional outcomes. We used Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology to generate recommendations. RESULTS: The initial literature search yielded 1998 articles, of which 33 formed the basis of the recommendations: PICO 1: We did not detect any significant positive or negative effect of ASM compared to no ASM on the outcomes of early seizure, late seizure, adverse events, or mortality. PICO 2: We did not detect any significant positive or negative effect of PHT/fPHT compared to LEV for early seizures or mortality, though point estimates suggest fewer late seizures and fewer adverse events with LEV. PICO 3: There were no significant differences in early or late seizures with longer versus shorter ASM use, though cognitive outcomes and adverse events appear worse with protracted use. CONCLUSIONS: Based on GRADE criteria, we suggest that ASM or no ASM may be used in patients hospitalized with moderate-severe TBI (weak recommendation, low quality of evidence). If used, we suggest LEV over PHT/fPHT (weak recommendation, very low quality of evidence) for a short duration (≤ 7 days, weak recommendation, low quality of evidence).

Identification of patients with epilepsy using automated electronic health records phenotyping.

OBJECTIVE: Unstructured data present in electronic health records (EHR) are a rich source of medical information; however, their abstraction is labor intensive. Automated EHR phenotyping (AEP) can reduce the need for manual chart review. We present an AEP model that is designed to automatically identify patients diagnosed with epilepsy. METHODS: The ground truth for model training and evaluation was captured from a combination of structured questionnaires filled out by physicians for a subset of patients and manual chart review using customized software. Modeling features included indicators of the presence of keywords and phrases in unstructured clinical notes, prescriptions for antiseizure medications (ASMs), International Classification of Diseases (ICD) codes for seizures and epilepsy, number of ASMs and epilepsy-related ICD codes, age, and sex. Data were randomly divided into training (70%) and hold-out testing (30%) sets, with distinct patients in each set. We trained regularized logistic regression and an extreme gradient boosting models. Model performance was measured using area under the receiver operating curve (AUROC) and area under the precision-recall curve (AUPRC), with 95% confidence intervals (CI) estimated via bootstrapping. RESULTS: Our study cohort included 3903 adults drawn from outpatient departments of nine hospitals between February 2015 and June 2022 (mean age = 47 ± 18 years, 57% women, 82% White, 84% non-Hispanic, 70% with epilepsy). The final models included 285 features, including 246 keywords and phrases captured from 8415 encounters. Both models achieved AUROC and AUPRC of 1 (95% CI = .99-1.00) in the hold-out testing set. SIGNIFICANCE: A machine learning-based AEP approach accurately identifies patients with epilepsy from notes, ICD codes, and ASMs. This model can enable large-scale epilepsy research using EHR databases.

Impact of acute symptomatic seizures and their management on patient-reported outcomes after stroke.

OBJECTIVE: Acute symptomatic seizures (ASyS) after stroke are not uncommon. However, the impact of ASyS and its management with anti-seizure medications (ASMs) on patient-reported outcome measures (PROMs) remains poorly investigated. The objective of our study is to evaluate the association between PROMs and ASyS and ASMs following stroke. METHODS: We performed a retrospective cohort study of all stroke patients who underwent inpatient continuous EEG (cEEG) monitoring performed due to suspected ASyS, including the ones with observed convulsive ASyS, from 04/01/2012 to 03/31/2018, who completed PROMs within 6 months of hospital discharge. Patient-reported outcome measures, including one Neuro-QoL and six PROMIS v1.0 domain scales, were completed by patients as the standard of care in ambulatory stroke clinics. Since ASMs are sometimes used without clearly diagnosed ASyS, we performed group comparisons based on ASM status at discharge, irrespective of their ASyS status. T-tests or Wilcoxon rank sum tests compared continuous variables across groups and chi-square tests or Fisher's exact tests were used for categorical variables. RESULTS: A total of 508 patients were included in the study [mean age 62.0 ± 14.1 years, 51.6% female; 244 (48.0%) ischemic stroke, 165 (32.5%) intracerebral hemorrhage, and 99 (19.5%) subarachnoid hemorrhage]. A total of 190 (37.4%) patients were discharged on ASMs. At the time of the first PROM, conducted a median of 47 (IQR = 33-78) days after the suspected ASyS, and 162 (31.9%) were on ASMs. ASM use was significantly higher in patients diagnosed with ASyS. Physical Function and Satisfaction with Social Roles and Activities were the most affected health domains. Patient-reported outcome measures were not significantly different between groups based on ASyS (electrographic and/or convulsive), ASM use at hospital discharge, or ASM status on the day of PROM completion. SIGNIFICANCE: There were no differences in multiple domain-specific PROMs in patients with recent stroke according to ASyS status or ASM use suggesting the possible lack of the former's sensitivity to detect their impact. Additional research is necessary to determine if there is a need for developing ASyS-specific PROMs.

Identifying inpatient hospitalizations with continuous electroencephalogram monitoring from administrative data.

BACKGROUND: Continuous electroencephalography (cEEG) is increasingly utilized in hospitalized patients to detect and treat seizures. Epidemiologic and observational studies using administrative datasets can provide insights into the comparative and cost effectiveness of cEEG utilization. Defining patient cohorts that underwent acute inpatient cEEG from administrative datasets is limited by the lack of validated codes differentiating elective epilepsy monitoring unit (EMU) admissions from acute inpatient hospitalization with cEEG utilization. Our aim was to develop hospital administrative data-based models to identify acute inpatient admissions with cEEG monitoring and distinguish them from EMU admissions. METHODS: This was a single center retrospective cohort study of adult (≥ 18 years old) inpatient admissions with a cEEG procedure (EMU or acute inpatient) between January 2016-April 2022. The gold standard for acute inpatient cEEG vs. EMU was obtained from the local EEG recording platform. An extreme gradient boosting model was trained to classify admissions as acute inpatient cEEG vs. EMU using administrative data including demographics, diagnostic and procedure codes, and medications. RESULTS: There were 9,523 patients in our cohort with 10,783 hospital admissions (8.5% EMU, 91.5% acute inpatient cEEG); with average age of 59 (SD 18.2) years; 46.2% were female. The model achieved an area under the receiver operating curve of 0.92 (95% CI [0.91-0.94]) and area under the precision-recall curve of 0.99 [0.98-0.99] for classification of acute inpatient cEEG. CONCLUSIONS: Our model has the potential to identify cEEG monitoring admissions in larger cohorts and can serve as a tool to enable large-scale, administrative data-based studies of EEG 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.

Anisocoria and Poor Pupil Reactivity by Quantitative Pupillometry in Patients With Intracranial Pathology.

OBJECTIVES: To describe the prevalence and associated risk factors of new onset anisocoria (new pupil size difference of at least 1 mm) and its subtypes: new onset anisocoria accompanied by abnormal and normal pupil reactivities in patients with acute neurologic injuries. DESIGN: We tested the association of patients who experienced new onset anisocoria subtypes with degree of midline shift using linear regression. We further explored differences between quantitative pupil characteristics associated with first-time new onset anisocoria and nonnew onset anisocoria at preceding observations using mixed effects logistic regression, adjusting for possible confounders. SETTING: All quantitative pupil observations were collected at two neuro-ICUs by nursing staff as standard of care. PATIENTS: We conducted a retrospective two-center study of adult patients with intracranial pathology in the ICU with at least a 24-hour stay and three or more quantitative pupil measurements between 2016 and 2018. MEASUREMENTS AND MAIN RESULTS: We studied 221 patients (mean age 58, 41% women). Sixty-three percent experienced new onset anisocoria. New onset anisocoria accompanied by objective evidence of abnormal pupil reactivity occurring at any point during hospitalization was significantly associated with maximum midline shift (ß = 2.27 per mm; p = 0.01). The occurrence of new onset anisocoria accompanied by objective evidence of normal pupil reactivity was inversely associated with death (odds ratio, 0.34; 95% CI, 0.16-0.71; p = 0.01) in adjusted analyses. Subclinical continuous pupil size difference distinguished first-time new onset anisocoria from nonnew onset anisocoria in up to four preceding pupil observations (or up to 8 hr prior). Minimum pupil reactivity between eyes also distinguished new onset anisocoria accompanied by objective evidence of abnormal pupil reactivity from new onset anisocoria accompanied by objective evidence of normal pupil reactivity prior to first-time new onset anisocoria occurrence. CONCLUSIONS: New onset anisocoria occurs in over 60% of patients with neurologic emergencies. Pupil reactivity may be an important distinguishing characteristic of clinically relevant new onset anisocoria phenotypes. New onset anisocoria accompanied by objective evidence of abnormal pupil reactivity was associated with midline shift, and new onset anisocoria accompanied by objective evidence of normal pupil reactivity had an inverse relationship with death. Distinct quantitative pupil characteristics precede new onset anisocoria occurrence and may allow for earlier prediction of neurologic decline. Further work is needed to determine whether quantitative pupillometry sensitively/specifically predicts clinically relevant anisocoria, enabling possible earlier treatments.

Automated Annotation of Epileptiform Burden and Its Association with Outcomes.

OBJECTIVE: This study was undertaken to determine the dose-response relation between epileptiform activity burden and outcomes in acutely ill patients. METHODS: A single center retrospective analysis was made of 1,967 neurologic, medical, and surgical patients who underwent >16 hours of continuous electroencephalography (EEG) between 2011 and 2017. We developed an artificial intelligence algorithm to annotate 11.02 terabytes of EEG and quantify epileptiform activity burden within 72 hours of recording. We evaluated burden (1) in the first 24 hours of recording, (2) in the 12-hours epoch with highest burden (peak burden), and (3) cumulatively through the first 72 hours of monitoring. Machine learning was applied to estimate the effect of epileptiform burden on outcome. Outcome measure was discharge modified Rankin Scale, dichotomized as good (0-4) versus poor (5-6). RESULTS: Peak epileptiform burden was independently associated with poor outcomes (p < 0.0001). Other independent associations included age, Acute Physiology and Chronic Health Evaluation II score, seizure on presentation, and diagnosis of hypoxic-ischemic encephalopathy. Model calibration error was calculated across 3 strata based on the time interval between last EEG measurement (up to 72 hours of monitoring) and discharge: (1) <5 days between last measurement and discharge, 0.0941 (95% confidence interval [CI] = 0.0706-0.1191); 5 to 10 days between last measurement and discharge, 0.0946 (95% CI = 0.0631-0.1290); >10 days between last measurement and discharge, 0.0998 (95% CI = 0.0698-0.1335). After adjusting for covariates, increase in peak epileptiform activity burden from 0 to 100% increased the probability of poor outcome by 35%. INTERPRETATION: Automated measurement of peak epileptiform activity burden affords a convenient, consistent, and quantifiable target for future multicenter randomized trials investigating whether suppressing epileptiform activity improves outcomes. ANN NEUROL 2021;90:300-311.

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.

Identifying Medicare Beneficiaries With Delirium.

BACKGROUND: Each year, thousands of older adults develop delirium, a serious, preventable condition. At present, there is no well-validated method to identify patients with delirium when using Medicare claims data or other large datasets. We developed and assessed the performance of classification algorithms based on longitudinal Medicare administrative data that included International Classification of Diseases, 10th Edition diagnostic codes. METHODS: Using a linked electronic health record (EHR)-Medicare claims dataset, 2 neurologists and 2 psychiatrists performed a standardized review of EHR records between 2016 and 2018 for a stratified random sample of 1002 patients among 40,690 eligible subjects. Reviewers adjudicated delirium status (reference standard) during this 3-year window using a structured protocol. We calculated the probability that each patient had delirium as a function of classification algorithms based on longitudinal Medicare claims data. We compared the performance of various algorithms against the reference standard, computing calibration-in-the-large, calibration slope, and the area-under-receiver-operating-curve using 10-fold cross-validation (CV). RESULTS: Beneficiaries had a mean age of 75 years, were predominately female (59%), and non-Hispanic Whites (93%); a review of the EHR indicated that 6% of patients had delirium during the 3 years. Although several classification algorithms performed well, a relatively simple model containing counts of delirium-related diagnoses combined with patient age, dementia status, and receipt of antipsychotic medications had the best overall performance [CV- calibration-in-the-large <0.001, CV-slope 0.94, and CV-area under the receiver operating characteristic curve (0.88 95% confidence interval: 0.84-0.91)]. CONCLUSIONS: A delirium classification model using Medicare administrative data and International Classification of Diseases, 10th Edition diagnosis codes can identify beneficiaries with delirium in large datasets.

Anti-seizure medication treatment and outcomes in acute ischemic stroke patients undergoing continuous EEG monitoring.

OBJECTIVES: To determine the association of anti-seizure medication (ASM) treatment with outcomes in acute ischemic stroke (AIS) patients undergoing continuous electroencephalography (cEEG). METHODS: Retrospective analysis of AIS patients admitted between 2012 and 2019. The following are the inclusion criteria: age ≥ 18 years and ≥ 16 h of cEEG within the first 7 days of admission. ASM treatment exposure was defined as > 48 h of treatment after the first 24 h of cEEG. The primary outcome measure was 90-day mortality, and the secondary outcome was 90-day functional recovery (Modified Ranking Scale 0-3). Propensity scores were used to adjust for baseline covariates and presence of epileptiform abnormalities (seizures, periodic and rhythmic patterns). RESULTS: One hundred thirteen patients met the inclusion criteria; 39 (34.5%) were exposed to ASM. ASM treatment was not associated with 90-day mortality (propensity adjusted HR 1.0 [0.31-3.27], p = 0.999) or functional outcomes (adjusted HR 0.99 [0.32-3.02], p = 0.989), compared to no treatment. CONCLUSIONS: In our study, ASM treatment in AIS patients with cEEG abnormalities was not significantly associated with a change in 90-day mortality and functional recovery. Larger comparative effectiveness studies are indicated to identify which acute ischemic stroke patients with cEEG abnormalities benefit most from ASM treatment.

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.

Antiseizure Medication Treatment and Outcomes in Patients with Subarachnoid Hemorrhage Undergoing Continuous EEG Monitoring.

BACKGROUND: Patients with aneurysmal subarachnoid hemorrhage (aSAH) with electroencephalographic epileptiform activity (seizures, periodic and rhythmic patterns, and sporadic discharges) are frequently treated with antiseizure medications (ASMs). However, the safety and effectiveness of ASM treatment for epileptiform activity has not been established. We used observational data to investigate the effectiveness of ASM treatment in patients with aSAH undergoing continuous electroencephalography (cEEG) to develop a causal hypothesis for testing in prospective trials. METHODS: This was a retrospective single-center cohort study of patients with aSAH admitted between 2011 and 2016. Patients underwent ≥ 24 h of cEEG within 4 days of admission. All patients received primary ASM prophylaxis until aneurysm treatment (typically within 24 h of admission). Treatment exposure was defined as reinitiation of ASMs after aneurysm treatment and cEEG initiation. We excluded patients with non-cEEG indications for ASMs (e.g., epilepsy, acute symptomatic seizures). Outcomes measures were 90-day mortality and good functional outcome (modified Rankin Scale scores 0-3). Propensity scores were used to adjust for baseline covariates and disease severity. RESULTS: Ninety-four patients were eligible (40 continued ASM treatment; 54 received prophylaxis only). ASM continuation was not significantly associated with higher 90-day mortality (propensity-adjusted hazard ratio [HR] = 2.01 [95% confidence interval (CI) 0.57-7.02]). ASM continuation was associated with lower likelihood for 90-day good functional outcome (propensity-adjusted HR = 0.39 [95% CI 0.18-0.81]). In a secondary analysis, low-intensity treatment (low-dose single ASM) was not significantly associated with mortality (propensity-adjusted HR = 0.60 [95% CI 0.10-3.59]), although it was associated with a lower likelihood of good outcome (propensity-adjusted HR = 0.37 [95% CI 0.15-0.91]), compared with prophylaxis. High-intensity treatment (high-dose single ASM, multiple ASMs, or anesthetics) was associated with higher mortality (propensity-adjusted HR = 6.80 [95% CI 1.67-27.65]) and lower likelihood for good outcomes (propensity-adjusted HR = 0.30 [95% CI 0.10-0.94]) compared with prophylaxis only. CONCLUSIONS: Our findings suggest the testable hypothesis that continuing ASMs in patients with aSAH with cEEG abnormalities does not improve functional outcomes. This hypothesis should be tested in prospective randomized studies.

Anticonvulsant Primary and Secondary Prophylaxis for Acute Ischemic Stroke Patients: A Decision Analysis.

Background and Purpose: We examined the impact of 3 anticonvulsant prophylaxis strategies on quality-adjusted life-years (QALYs) among patients with an incident acute ischemic stroke. Methods: We created a decision tree to evaluate 3 strategies: (1) long-term primary prophylaxis; (2) short-term secondary prophylaxis after an early seizure with lifetime prophylaxis if persistent or late seizures (LSs) developed; and (3) long-term secondary prophylaxis if either early, late, or persistent seizures developed. The outcome was quality-adjusted life expectancy (QALY). We created 4 base cases to simulate common clinical scenarios: (1) female patient aged 40 years with a 2% or 11% lifetime risk of an LS and a 33% lifetime risk of an adverse drug reaction (ADR); (2) male patient aged 65 years with a 6% or 29% LS risk and 60% ADR risk; (3) male patient aged 50 years with an 18% or 65% LS risk and 33% ADR risk; and (4) female patient aged 80 years with a 29% or 83% LS risk and 80% ADR risk. In sensitivity analyses, we altered the parameters and assumptions. Results: Across all 4 base cases, primary prophylaxis yielded the fewest QALYs when compared with secondary prophylaxis. For example, under scenario 1, strategies 2 and 3 resulted in 7.17 QALYs each, but strategy 1 yielded only 6.91 QALYs. Under scenario 4, strategies 2 and 3 yielded 2.85 QALYs compared with 1.40 QALYs for strategy 1. Under scenarios in which patients had higher ADR risks, strategy 2 led to the most QALYs. Conclusions: Short-term therapy with continued anticonvulsant prophylaxis only after postischemic stroke seizures arise dominates lifetime primary prophylaxis in all scenarios examined. Our findings reinforce the necessity of close follow-up and discontinuation of anticonvulsant seizure prophylaxis started during acute ischemic stroke hospitalization.

Electrographic predictors of successful weaning from anaesthetics in refractory status epilepticus.

Intravenous third-line anaesthetic agents are typically titrated in refractory status epilepticus to achieve either seizure suppression or burst suppression on continuous EEG. However, the optimum treatment paradigm is unknown and little data exist to guide the withdrawal of anaesthetics in refractory status epilepticus. Premature withdrawal of anaesthetics risks the recurrence of seizures, whereas the prolonged use of anaesthetics increases the risk of treatment-associated adverse effects. This study sought to measure the accuracy of features of EEG activity during anaesthetic weaning in refractory status epilepticus as predictors of successful weaning from intravenous anaesthetics. We prespecified a successful anaesthetic wean as the discontinuation of intravenous anaesthesia without developing recurrent status epilepticus, and a wean failure as either recurrent status epilepticus or the resumption of anaesthesia for the purpose of treating an EEG pattern concerning for incipient status epilepticus. We evaluated two types of features as predictors of successful weaning: spectral components of the EEG signal, and spatial-correlation-based measures of functional connectivity. The results of these analyses were used to train a classifier to predict wean outcome. Forty-seven consecutive anaesthetic weans (23 successes, 24 failures) were identified from a single-centre cohort of patients admitted with refractory status epilepticus from 2016 to 2019. Spectral components of the EEG revealed no significant differences between successful and unsuccessful weans. Analysis of functional connectivity measures revealed that successful anaesthetic weans were characterized by the emergence of larger, more densely connected, and more highly clustered spatial functional networks, yielding 75.5% (95% confidence interval: 73.1-77.8%) testing accuracy in a bootstrap analysis using a hold-out sample of 20% of data for testing and 74.6% (95% confidence interval 73.2-75.9%) testing accuracy in a secondary external validation cohort, with an area under the curve of 83.3%. Distinct signatures in the spatial networks of functional connectivity emerge during successful anaesthetic liberation in status epilepticus; these findings are absent in patients with anaesthetic wean failure. Identifying features that emerge during successful anaesthetic weaning may allow faster and more successful anaesthetic liberation after refractory status epilepticus.

Electroencephalography, Hospital Complications, and Longitudinal Outcomes After Subarachnoid Hemorrhage.

BACKGROUND: Following non-traumatic subarachnoid hemorrhage (SAH), in-hospital delayed cerebral ischemia is predicted by two chief events on continuous EEG (cEEG): new or worsening epileptiform abnormalities (EAs) and deterioration of cEEG background frequencies. We evaluated the association between longitudinal outcomes and these cEEG biomarkers. We additionally evaluated the association between longitudinal outcomes and other in-hospital complications. METHODS: Patients with nontraumatic SAH undergoing ≥ 3 days of cEEG monitoring were enrolled in a prospective study evaluating longitudinal outcomes. Modified Rankin Scale (mRS) was assessed at discharge, and at 3- and 6-month follow-up time points. Adjusting for baseline severity in a cumulative proportional odds model, we modeled the mRS ordinally and measured the association between mRS and two forms of in-hospital cEEG deterioration: (1) cEEG evidence of new or worsening epileptiform abnormalities and (2) cEEG evidence of new background deterioration. We compared the magnitude of these associations at each time point with the association between mRS and other in-hospital complications: (1) delayed cerebral ischemia (DCI), (2) hospital-acquired infections (HAI), and (3) hydrocephalus. In a secondary analysis, we employed a linear mixed effects model to examine the association of mRS over time (dichotomized as 0-3 vs. 4-6) with both biomarkers of cEEG deterioration and with other in-hospital complications. RESULTS: In total, 175 mRS assessments were performed in 59 patients. New or worsening EAs developed in 23 (39%) patients, and new background deterioration developed in 24 (41%). Among cEEG biomarkers, new or worsening EAs were independently associated with mRS at discharge, 3, and 6 months, respectively (adjusted cumulative proportional odds 4.99, 95% CI 1.60-15.6; 3.28, 95% CI 1.14-9.5; and 2.71, 95% CI 0.95-7.76), but cEEG background deterioration lacked an association. Among hospital complications, DCI was associated with discharge, 3-, and 6-month outcomes (adjusted cumulative proportional odds 4.75, 95% CI 1.64-13.8; 3.4; 95% CI 1.24-9.01; and 2.45, 95% CI 0.94-6.6), but HAI and hydrocephalus lacked an association. The mixed effects model demonstrated that these associations were sustained over longitudinal assessments without an interaction with time. CONCLUSION: Although new or worsening EAs and cEEG background deterioration have both been shown to predict DCI, only new or worsening EAs are associated with a sustained impairment in functional outcome. This novel finding raises the potential for identifying therapeutic targets that may also influence outcomes.