Extracting seizure control metrics from clinic notes of patients with epilepsy: A natural language processing approach.

OBJECTIVES: Monitoring seizure control metrics is key to clinical care of patients with epilepsy. Manually abstracting these metrics from unstructured text in electronic health records (EHR) is laborious. We aimed to abstract the date of last seizure and seizure frequency from clinical notes of patients with epilepsy using natural language processing (NLP). METHODS: We extracted seizure control metrics from notes of patients seen in epilepsy clinics from two hospitals in Boston. Extraction was performed with the pretrained model RoBERTa_for_seizureFrequency_QA, for both date of last seizure and seizure frequency, combined with regular expressions. We designed the algorithm to categorize the timing of last seizure ("today", "1-6 days ago", "1-4 weeks ago", "more than 1-3 months ago", "more than 3-6 months ago", "more than 6-12 months ago", "more than 1-2 years ago", "more than 2 years ago") and seizure frequency ("innumerable", "multiple", "daily", "weekly", "monthly", "once per year", "less than once per year"). Our ground truth consisted of structured questionnaires filled out by physicians. Model performance was measured using the areas under the receiving operating characteristic curve (AUROC) and precision recall curve (AUPRC) for categorical labels, and median absolute error (MAE) for ordinal labels, with 95 % confidence intervals (CI) estimated via bootstrapping. RESULTS: Our cohort included 1773 adult patients with a total of 5658 visits with reported seizure control metrics, seen in epilepsy clinics between December 2018 and May 2022. The cohort average age was 42 years old, the majority were female (57 %), White (81 %) and non-Hispanic (85 %). The models achieved an MAE (95 % CI) for date of last seizure of 4 (4.00-4.86) weeks, and for seizure frequency of 0.02 (0.02-0.02) seizures per day. CONCLUSIONS: Our NLP approach demonstrates that the extraction of seizure control metrics from EHR is feasible allowing for large-scale EHR research.

Antiseizure Medication Use and Outcomes After Suspected or Confirmed Acute Symptomatic Seizures.

Importance: Antiseizure medications (ASMs) are frequently prescribed for acute symptomatic seizures and epileptiform abnormalities (EAs; eg, periodic or rhythmic patterns). There are limited data on factors associated with ASM use and their association with outcomes. Objectives: To determine factors associated with ASM use in patients with confirmed or suspected acute symptomatic seizures undergoing continuous electroencephalography, and to explore the association of ASMs with outcomes. Design, Setting, and Participants: This multicenter cohort study was performed between July 1 and September 30, 2021, at 5 US centers of the Post Acute Symptomatic Seizure Investigation and Outcomes Network. After screening 1717 patients, the study included 1172 hospitalized adults without epilepsy who underwent continuous electroencephalography after witnessed or suspected acute symptomatic seizures. Data analysis was performed from November 14, 2023, to February 2, 2024. Exposure: ASM treatment (inpatient ASM continuation ≥48 hours). Main Outcomes and Measures: Factors associated with (1) ASM treatment, (2) discharge ASM prescription, and (3) discharge and 3-month Glasgow Outcome Scale score of 4 or 5 were ascertained. Results: A total of 1172 patients (median [IQR] age, 64 [52-75] years; 528 [45%] female) were included. Among them, 285 (24%) had clinical acute symptomatic seizures, 107 (9%) had electrographic seizures, and 364 (31%) had EAs; 532 (45%) received ASM treatment. Among 922 patients alive at discharge, 288 (31%) were prescribed ASMs. The respective frequencies of inpatient ASM treatment and discharge prescription were 82% (233 of 285) and 69% (169 of 246) for patients with clinical acute symptomatic seizures, 96% (103 of 107) and 95% (61 of 64) for electrographic seizures, and 64% (233 of 364) and 48% (128 of 267) for EAs. On multivariable analysis, acute and progressive brain injuries were independently associated with increased odds of inpatient ASM treatment (odds ratio [OR], 3.86 [95% CI, 2.06-7.32] and 8.37 [95% CI, 3.48-20.80], respectively) and discharge prescription (OR, 2.26 [95% CI, 1.04-4.98] and 10.10 [95% CI, 3.94-27.00], respectively). Admission to the neurology or neurosurgery service (OR, 2.56 [95% CI, 1.08-6.18]) or to the neurological intensive care unit (OR, 7.98 [95% CI, 3.49-19.00]) was associated with increased odds of treatment. Acute symptomatic seizures and EAs were significantly associated with increased odds of ASM treatment (OR, 14.30 [95% CI, 8.52-24.90] and 2.30 [95% CI, 1.47-3.61], respectively) and discharge prescription (OR, 12.60 [95% CI, 7.37-22.00] and 1.72 [95% CI, 1.00-2.97], respectively). ASM treatment was not associated with outcomes at discharge (OR, 0.96 [95% CI, 0.61-1.52]) or at 3 months after initial presentation (OR, 1.26 [95% CI, 0.78-2.04]). Among 623 patients alive and with complete data at 3 months after discharge, 30 (5%) had postdischarge seizures, 187 (30%) were receiving ASMs, and 202 (32%) had all-cause readmissions. Conclusions and Relevance: This study suggests that etiology and electrographic findings are associated with ASM treatment for acute symptomatic seizures and EAs; ASM treatment was not associated with functional outcomes. Comparative effectiveness studies are indicated to identify which patients may benefit from ASMs and to determine the optimal treatment duration.

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.

How many patients do you need? Investigating trial designs for anti-seizure treatment in acute brain injury patients.

BACKGROUND/OBJECTIVES: Epileptiform activity (EA), including seizures and periodic patterns, worsens outcomes in patients with acute brain injuries (e.g., aneurysmal subarachnoid hemorrhage [aSAH]). Randomized control trials (RCTs) assessing anti-seizure interventions are needed. Due to scant drug efficacy data and ethical reservations with placebo utilization, and complex physiology of acute brain injury, RCTs are lacking or hindered by design constraints. We used a pharmacological model-guided simulator to design and determine the feasibility of RCTs evaluating EA treatment. METHODS: In a single-center cohort of adults (age >18) with aSAH and EA, we employed a mechanistic pharmacokinetic-pharmacodynamic framework to model treatment response using observational data. We subsequently simulated RCTs for levetiracetam and propofol, each with three treatment arms mirroring clinical practice and an additional placebo arm. Using our framework, we simulated EA trajectories across treatment arms. We predicted discharge modified Rankin Scale as a function of baseline covariates, EA burden, and drug doses using a double machine learning model learned from observational data. Differences in outcomes across arms were used to estimate the required sample size. RESULTS: Sample sizes ranged from 500 for levetiracetam 7 mg/kg versus placebo, to >4000 for levetiracetam 15 versus 7 mg/kg to achieve 80% power (5% type I error). For propofol 1 mg/kg/h versus placebo, 1200 participants were needed. Simulations comparing propofol at varying doses did not reach 80% power even at samples >1200. CONCLUSIONS: Our simulations using drug efficacy show sample sizes are infeasible, even for potentially unethical placebo-control trials. We highlight the strength of simulations with observational data to inform the null hypotheses and propose use of this simulation-based RCT paradigm to assess the feasibility of future trials of anti-seizure treatment in acute brain injury.

Automated Extraction of Stroke Severity from Unstructured Electronic Health Records using Natural Language Processing.

BACKGROUND: Multi-center electronic health records (EHR) can support quality improvement initiatives and comparative effectiveness research in stroke care. However, limitations of EHR-based research include challenges in abstracting key clinical variables from non-structured data at scale. This is further compounded by missing data. Here we develop a natural language processing (NLP) model that automatically reads EHR notes to determine the NIH stroke scale (NIHSS) score of patients with acute stroke. METHODS: The study included notes from acute stroke patients (>= 18 years) admitted to the Massachusetts General Hospital (MGH) (2015-2022). The MGH data were divided into training (70%) and hold-out test (30%) sets. A two-stage model was developed to predict the admission NIHSS. A linear model with the least absolute shrinkage and selection operator (LASSO) was trained within the training set. For notes in the test set where the NIHSS was documented, the scores were extracted using regular expressions (stage 1), for notes where NIHSS was not documented, LASSO was used for prediction (stage 2). The reference standard for NIHSS was obtained from Get With The Guidelines Stroke Registry. The two-stage model was tested on the hold-out test set and validated in the MIMIC-III dataset (Medical Information Mart for Intensive Care-MIMIC III 2001-2012) v1.4, using root mean squared error (RMSE) and Spearman correlation (SC). RESULTS: We included 4,163 patients (MGH = 3,876; MIMIC = 287); average age of 69 [SD 15] years; 53% male, and 72% white. 90% patients had ischemic stroke and 10% hemorrhagic stroke. The two-stage model achieved a RMSE [95% CI] of 3.13 [2.86-3.41] (SC = 0.90 [0.88-0. 91]) in the MGH hold-out test set and 2.01 [1.58-2.38] (SC = 0.96 [0.94-0.97]) in the MIMIC validation set. CONCLUSIONS: The automatic NLP-based model can enable large-scale stroke severity phenotyping from EHR and therefore support real-world quality improvement and comparative effectiveness studies in stroke.

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.

Predictors of follow-up care for critically-ill patients with seizures and epileptiform abnormalities on EEG monitoring.

OBJECTIVE: Post-hospitalization follow-up visits are crucial for preventing long-term complications. Patients with electrographic epileptiform abnormalities (EA) including seizures and periodic and rhythmic patterns are especially in need of follow-up for long-term seizure risk stratification and medication management. We sought to identify predictors of follow-up. METHODS: This is a retrospective cohort study of all patients (age ≥ 18 years) admitted to intensive care units that underwent continuous EEG (cEEG) monitoring at a single center between 01/2016-12/2019. Patients with EAs were included. Clinical and demographic variables were recorded. Follow-up status was determined using visit records 6-month post discharge, and visits were stratified as outpatient follow-up, neurology follow-up, and inpatient readmission. Lasso feature selection analysis was performed. RESULTS: 723 patients (53 % female, mean (std) age of 62.3 (16.4) years) were identified from cEEG records with 575 (79 %) surviving to discharge. Of those discharged, 450 (78 %) had outpatient follow-up, 316 (55 %) had a neurology follow-up, and 288 (50 %) were readmitted during the 6-month period. Discharge on antiseizure medications (ASM), younger age, admission to neurosurgery, and proximity to the hospital were predictors of neurology follow-up visits. Discharge on ASMs, along with longer length of stay, younger age, emergency admissions, and higher illness severity were predictors of readmission. SIGNIFICANCE: ASMs at discharge, demographics (age, address), hospital care teams, and illness severity determine probability of follow-up. Parameters identified in this study may help healthcare systems develop interventions to improve care transitions for critically-ill patients with seizures and other EA.

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.

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.

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).

Guidelines for seizure prophylaxis in adults hospitalized with moderate­severe traumatic brain injury: a clinical practice guideline for health care professionals from the Neurocritical Care Society

There is practice heterogeneity in the use, type, and duration of prophylactic antiseizure medications (ASMs) in patients with moderate­severe traumatic brain injury (TBI). We conducted a systematic review and meta-analysis of articles assessing ASM prophylaxis in adults with moderate­severe 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 moderate­severe 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. 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. Based on GRADE criteria, we suggest that ASM or no ASM may be used in patients hospitalized with moderate­severe 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).

Characteristics and Attendance of Patients Eligible for the PASS Clinic: A Transition of Care Model After Acute Symptomatic Seizures.

Background and Objectives: Most acute symptomatic seizure (ASyS) patients stay on antiseizure medications (ASM) long-term, despite low epilepsy development risk. The Post-Acute Symptomatic Seizure (PASS) clinic is a transition of care model for ASyS patients who individualize ASM management with the goal of a safe deprescription. We evaluated patients discharged on ASMs after a witnessed or suspected ASyS to analyze their PASS clinic visit attendance and its predictors. Methods: A single-center, retrospective cohort study of adults without epilepsy who were discharged from January 1, 2019, to September 30, 2019, on first-time ASMs due to witnessed or suspected ASyS (PASS clinic-eligible). We fit a cause-specific Cox proportional hazards model to analyze factors associated with PASS clinic attendance, which depends on survival in this patient population that has a high early postdischarge mortality (a competing risk). We checked for multicollinearity and the assumption of proportional hazards. Results: Among 307 PASS clinic-eligible patients, 95 (30.9%) attended the clinic and 136 (44.3%) died during a median follow-up of 14 months (interquartile range = 2-34). ASyS occurred in 60.2% (convulsive 47%; electrographic 26.7%) of patients. ASMs were continued in the absence of ASyS or epileptiform abnormalities (EAs) in 27% of patients. Multivariable analysis revealed that the presence of EAs (HR = 1.69, 95% CI 1.10-2.59), PASS clinic appointments provided before discharge (HR = 3.39, 95% CI 2.15-5.33), and less frequently noted ASyS etiologies such as autoimmune encephalitis (HR = 2.03, 95% CI 1.07-3.86) were associated with an increased clinic attendance rate. Medicare/Medicaid insurance (HR = 0.43, 95% CI 0.24-0.78, p = 0.005) and the presence of progressive brain injury (i.e., tumors; HR = 0.55, 95% CI 0.32-0.95, p = 0.032) were associated with reduced rate of PASS clinic attendance. Discussion: Our real-world data highlight the need for appropriate postdischarge follow-up of ASyS patients, which can be fulfilled by the PASS clinic model. Modest PASS clinic attendance can be significantly improved by adhering to a structured discharge planning process whereby appointments are provided before discharge. Future research comparing patient outcomes, specifically safe ASM discontinuation in a PASS clinic model to routine clinical care, is needed.

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.

Real-World Continuous EEG Utilization and Outcomes in Hospitalized Patients With Acute Cerebrovascular Diseases.

PURPOSE: Continuous electroencephalography (cEEG) is recommended for hospitalized patients with cerebrovascular diseases and suspected seizures or unexplained neurologic decline. We sought to (1) identify areas of practice variation in cEEG utilization, (2) determine predictors of cEEG utilization, (3) evaluate whether cEEG utilization is associated with outcomes in patients with cerebrovascular diseases. METHODS: This cohort study of the Premier Healthcare Database (2014-2020), included hospitalized patients age >18 years with cerebrovascular diseases (identified by ICD codes). Continuous electroencephalography was identified by International Classification of Diseases (ICD)/Current Procedural Terminology (CPT) codes. Multivariable lasso logistic regression was used to identify predictors of cEEG utilization and in-hospital mortality. Propensity score-matched analysis was performed to determine the relation between cEEG use and mortality. RESULTS: 1,179,471 admissions were included; 16,777 (1.4%) underwent cEEG. Total number of cEEGs increased by 364% over 5 years (average 32%/year). On multivariable analysis, top five predictors of cEEG use included seizure diagnosis, hospitals with >500 beds, regions Northeast and South, and anesthetic use. Top predictors of mortality included use of mechanical ventilation, vasopressors, anesthetics, antiseizure medications, and age. Propensity analysis showed that cEEG was associated with lower in-hospital mortality (Average Treatment Effect -0.015 [95% confidence interval -0.028 to -0.003], Odds ratio 0.746 [95% confidence interval, 0.618-0.900]). CONCLUSIONS: There has been a national increase in cEEG utilization for hospitalized patients with cerebrovascular diseases, with practice variation. cEEG utilization was associated with lower in-hospital mortality. Larger comparative studies of cEEG-guided treatments are indicated to inform best practices, guide policy changes for increased access, and create guidelines on triaging and transferring patients to centers with cEEG capability.

Brain health scores to predict neurological outcomes from electronic health records.

BACKGROUND: Preserving brain health is a critical priority in primary care, yet screening for these risk factors in face-to-face primary care visits is challenging to scale to large populations. We aimed to develop automated brain health risk scores calculated from data in the electronic health record (EHR) enabling population-wide brain health screening in advance of patient care visits. METHODS: This retrospective cohort study included patients with visits to an outpatient neurology clinic at Massachusetts General Hospital, between January 2010 and March 2021. Survival analysis with an 11-year follow-up period was performed to predict the risk of intracranial hemorrhage, ischemic stroke, depression, death and composite outcome of dementia, Alzheimer's disease, and mild cognitive impairment. Variables included age, sex, vital signs, laboratory values, employment status and social covariates pertaining to marital, tobacco and alcohol status. Random sampling was performed to create a training (70%) set for hyperparameter tuning in internal 5-fold cross validation and an external hold-out testing (30%) set of patients, both stratified by age. Risk ratios for high and low risk groups were evaluated in the hold-out test set, using 1000 bootstrapping iterations to calculate 95% confidence intervals (CI). RESULTS: The cohort comprised 17,040 patients with an average age of 49 ± 15.6 years; majority were males (57 %), White (78 %) and non-Hispanic (80 %). The low and high groups average risk ratios [95 % CI] were: intracranial hemorrhage 0.46 [0.45-0.48] and 2.07 [1.95-2.20], ischemic stroke 0.57 [0.57-0.59] and 1.64 [1.52-1.69], depression 0.68 [0.39-0.74] and 1.29 [0.78-1.38], composite of dementia 0.27 [0.26-0.28] and 3.52 [3.18-3.81] and death 0.24 [0.24-0.24] and 3.96 [3.91-4.00]. CONCLUSIONS: Simple risk scores derived from routinely collected EHR accurately quantify the risk of developing common neurologic and psychiatric diseases. These scores can be computed automatically, prior to medical care visits, and may thus be useful for large-scale brain health screening.

How Many Patients Do You Need? Investigating Trial Designs for Anti-Seizure Treatment in Acute Brain Injury Patients.

Objectives: Epileptiform activity (EA) worsens outcomes in patients with acute brain injuries (e.g., aneurysmal subarachnoid hemorrhage [aSAH]). Randomized trials (RCTs) assessing anti-seizure interventions are needed. Due to scant drug efficacy data and ethical reservations with placebo utilization, RCTs are lacking or hindered by design constraints. We used a pharmacological model-guided simulator to design and determine feasibility of RCTs evaluating EA treatment. Methods: In a single-center cohort of adults (age >18) with aSAH and EA, we employed a mechanistic pharmacokinetic-pharmacodynamic framework to model treatment response using observational data. We subsequently simulated RCTs for levetiracetam and propofol, each with three treatment arms mirroring clinical practice and an additional placebo arm. Using our framework we simulated EA trajectories across treatment arms. We predicted discharge modified Rankin Scale as a function of baseline covariates, EA burden, and drug doses using a double machine learning model learned from observational data. Differences in outcomes across arms were used to estimate the required sample size. Results: Sample sizes ranged from 500 for levetiracetam 7 mg/kg vs placebo, to >4000 for levetiracetam 15 vs. 7 mg/kg to achieve 80% power (5% type I error). For propofol 1mg/kg/hr vs. placebo 1200 participants were needed. Simulations comparing propofol at varying doses did not reach 80% power even at samples >1200. Interpretation: Our simulations using drug efficacy show sample sizes are infeasible, even for potentially unethical placebo-control trials. We highlight the strength of simulations with observational data to inform the null hypotheses and assess feasibility of future trials of EA treatment.

Effects of epileptiform activity on discharge outcome in critically ill patients in the USA: a retrospective cross-sectional study.

BACKGROUND: Epileptiform activity is associated with worse patient outcomes, including increased risk of disability and death. However, the effect of epileptiform activity on neurological outcome is confounded by the feedback between treatment with antiseizure medications and epileptiform activity burden. We aimed to quantify the heterogeneous effects of epileptiform activity with an interpretability-centred approach. METHODS: We did a retrospective, cross-sectional study of patients in the intensive care unit who were admitted to Massachusetts General Hospital (Boston, MA, USA). Participants were aged 18 years or older and had electrographic epileptiform activity identified by a clinical neurophysiologist or epileptologist. The outcome was the dichotomised modified Rankin Scale (mRS) at discharge and the exposure was epileptiform activity burden defined as mean or maximum proportion of time spent with epileptiform activity in 6 h windows in the first 24 h of electroencephalography. We estimated the change in discharge mRS if everyone in the dataset had experienced a specific epileptiform activity burden and were untreated. We combined pharmacological modelling with an interpretable matching method to account for confounding and epileptiform activity-antiseizure medication feedback. The quality of the matched groups was validated by the neurologists. FINDINGS: Between Dec 1, 2011, and Oct 14, 2017, 1514 patients were admitted to Massachusetts General Hospital intensive care unit, 995 (66%) of whom were included in the analysis. Compared with patients with a maximum epileptiform activity of 0 to less than 25%, patients with a maximum epileptiform activity burden of 75% or more when untreated had a mean 22·27% (SD 0·92) increased chance of a poor outcome (severe disability or death). Moderate but long-lasting epileptiform activity (mean epileptiform activity burden 2% to <10%) increased the risk of a poor outcome by mean 13·52% (SD 1·93). The effect sizes were heterogeneous depending on preadmission profile-eg, patients with hypoxic-ischaemic encephalopathy or acquired brain injury were more adversely affected compared with patients without these conditions. INTERPRETATION: Our results suggest that interventions should put a higher priority on patients with an average epileptiform activity burden 10% or greater, and treatment should be more conservative when maximum epileptiform activity burden is low. Treatment should also be tailored to individual preadmission profiles because the potential for epileptiform activity to cause harm depends on age, medical history, and reason for admission. FUNDING: National Institutes of Health and National Science Foundation.

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.

Association of Epileptiform Activity With Outcomes in Toxic-Metabolic Encephalopathy.

The clinical significance of epileptiform abnormalities (EAs) specific to toxic-metabolic encephalopathy (TME) is unknown. OBJECTIVES: To quantify EA burden in patients with TME and its association with neurologic outcomes. DESIGN SETTING AND PARTICIPANT: This is a retrospective study. A cohort of patients with TME and EA (positive) were age, Sequential Organ Failure Assessment Score, Acute Physiology and Chronic Health Evaluation II (APACHE-II) score matched to a cohort of TME patients without EA (control). Univariate analysis compared EA-positive patients against controls. Multivariable logistical regression adjusting for underlying disease etiology was performed to examine the relationship between EA burden and probability of poor neurologic outcome (modified Rankin Score [mRS] 4-6) at discharge. Consecutive admissions to inpatient floors or ICUs that underwent continuous electroencephalography (cEEG) monitoring at a single center between 2012 and 2019. Inclusion criteria were 1) patients with TME diagnosis, 2) age greater than 18 years, and 3) greater than or equal to 16 hours of cEEG. Patients with acute brain injury and cardiac arrest were excluded. MAIN OUTCOMES AND MEASURES: Poor neurologic outcome defined by mRS (mRS 4-6). RESULTS: One hundred sixteen patients were included, 58 with EA and 58 controls without EA, where matching was performed on age and APACHE-II score. The median age was 66 (Q1-Q3, 57-75) and median APACHE II score was 18 (Q1-Q3, 13-22). Overall cohort discharge mortality was 22% and 70% had a poor neurologic outcome. Peak EA burden was defined as the 12-hour window of recording with the highest prevalence of EAs. In multivariable analysis adjusted for Charlson Comorbidity Index and primary diagnosis, presence of EAs was associated with poor outcome (odds ratio 3.89; CI [1.05-14.2], p = 0.041). Increase in peak EA burden from 0% to 100% increased probability of poor discharge neurologic outcome by 30%. CONCLUSIONS AND RELEVANCE: Increasing burden of EA is associated with worse discharge outcomes in patients with TME. Future studies are needed to determine whether short-term treatment with anti-seizure medications while medically treating the underlying metabolic derangement improves outcomes.