Efficiency and safety of high-dose undiluted intravenous push levetiracetam loading doses compared to intravenous infusion in seizing patients: A retrospective cohort study.

OBJECTIVE: Intravenous (IV) push (IVP) is an alternative administration method for levetiracetam, but evidence evaluating it compared to IV piggyback (IVPB) for loading doses in acutely seizing patients is limited, particularly in patients with status epilepticus (SE). This study aimed to compare the efficiency and safety of IVP versus IVPB levetiracetam loading doses. METHODS: This was a single-center sequential retrospective study conducted in adult (≥18 years) patients who received an IV levetiracetam loading dose (>2000 mg or ≥20 mg/kg) for acute or suspected seizure. The primary outcome was time to administration, compared between doses given as IVP versus IVPB. Secondary outcomes included rates of adverse events (AEs), rescue benzodiazepine or antiseizure medication administration, intubation, and intensive care unit (ICU) admission between groups. RESULTS: A total of 246 patients were included; 116 received IVP and 130 received IVPB loading doses. Median age was 56 years; most patients were male (62%) and White (60%) and had witnessed seizures (67%). Doses were administered for SE in 32 (27.5%) and 46 (35.4%) patients in the IVP and IVPB arms, respectively. Median time to administration was shorter in the IVP group (12 vs. 38 min, p < .001). Bradycardia (1.7% vs. 2.3%, p = .99), hypotension (7.8% vs. 12%, p = .30), sedation (6% vs. 12.3%, p = .09), intubation (10% vs. 8%, p = .37), ICU admission (32% vs. 39%, p = .31), and rescue medication administration (8.6% vs. 14.6% p = .10) were similar between groups. In SE patients, IVP was associated with shorter time to administration (12 vs. 44 min, p = .003) and lower odds of ICU admission after adjustment for age, dose, Status Epilepticus Severity Score, and seizure history (adjusted odds ratio = .23, 95% confidence interval = .06-.81). SIGNIFICANCE: IVP reduced time to levetiracetam administration versus IVPB and was not associated with more AEs. Rescue agent use, intubation, and ICU admission were similar between arms, but IVP may reduce ICU admissions in SE patients. Prospective studies should assess the effectiveness of IVP versus IVPB.

Sedation Intensity in Patients with Moderate to Severe Traumatic Brain Injury in the Intensive Care Unit: A TRACK-TBI Cohort Study.

BACKGROUND: Interventions to reduce intracranial pressure (ICP) in patients with traumatic brain injury (TBI) are multimodal but variable, including sedation-dosing strategies. This article quantifies the different sedation intensities administered in patients with moderate to severe TBI (msTBI) using the therapy intensity level (TIL) across different intensive care units (ICUs), including the use of additional ICP-lowering therapies. METHODS: Within the prospective Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) study, we performed a retrospective analysis of adult patients with msTBI admitted to an ICU for a least 5 days from seven US level 1 trauma centers who received invasive ICP monitoring and intravenous sedation. Sedation intensity was classified prospectively as one of three ordinal levels as part of the validated TIL score, which were collected at least once a day. RESULTS: A total of 127 patients met inclusion criteria (mean age 41.6 ± 17.7 years; 20% female). The median Injury Severity Score was 27 (interquartile range 17-33), with a median admission Glasgow Coma Score of 3 (interquartile range 3-7); 104 patients had severe TBI (82%), and 23 patients had moderate TBI (18%). The sedation intensity score was highest on the first ICU day (2.69 ± 1.78), independent of patient severity. Time to reaching each sedation intensity level varied by site. Sedation level I was reached within 24 h for all sites, but sedation levels II and III were reached variably between days 1 and 3. Sedation level III was never reached by two of seven sites. The total TIL score was highest on the first ICU day, with a modest decrease for each subsequent ICU day, but there was high site-specific practice-pattern variation. CONCLUSIONS: Intensity of sedation and other therapies for elevated ICP for patients with msTBI demonstrate large practice-pattern variation across level 1 trauma centers within the TRACK-TBI cohort study, independent of patient severity. Optimizing sedation strategies using patient-specific physiologic and pathoanatomic information may optimize patient outcomes.

Utility and rationale for continuous EEG monitoring: a primer for the general intensivist.

This review offers a comprehensive guide for general intensivists on the utility of continuous EEG (cEEG) monitoring for critically ill patients. Beyond the primary role of EEG in detecting seizures, this review explores its utility in neuroprognostication, monitoring neurological deterioration, assessing treatment responses, and aiding rehabilitation in patients with encephalopathy, coma, or other consciousness disorders. Most seizures and status epilepticus (SE) events in the intensive care unit (ICU) setting are nonconvulsive or subtle, making cEEG essential for identifying these otherwise silent events. Imaging and invasive approaches can add to the diagnosis of seizures for specific populations, given that scalp electrodes may fail to identify seizures that may be detected by depth electrodes or electroradiologic findings. When cEEG identifies SE, the risk of secondary neuronal injury related to the time-intensity "burden" often prompts treatment with anti-seizure medications. Similarly, treatment may be administered for seizure-spectrum activity, such as periodic discharges or lateralized rhythmic delta slowing on the ictal-interictal continuum (IIC), even when frank seizures are not evident on the scalp. In this setting, cEEG is utilized empirically to monitor treatment response. Separately, cEEG has other versatile uses for neurotelemetry, including identifying the level of sedation or consciousness. Specific conditions such as sepsis, traumatic brain injury, subarachnoid hemorrhage, and cardiac arrest may each be associated with a unique application of cEEG; for example, predicting impending events of delayed cerebral ischemia, a feared complication in the first two weeks after subarachnoid hemorrhage. After brief training, non-neurophysiologists can learn to interpret quantitative EEG trends that summarize elements of EEG activity, enhancing clinical responsiveness in collaboration with clinical neurophysiologists. Intensivists and other healthcare professionals also play crucial roles in facilitating timely cEEG setup, preventing electrode-related skin injuries, and maintaining patient mobility during monitoring.

Deriving Automated Device Metadata From Intracranial Pressure Waveforms: A Transforming Research and Clinical Knowledge in Traumatic Brain Injury ICU Physiology Cohort Analysis.

IMPORTANCE: Treatment for intracranial pressure (ICP) has been increasingly informed by machine learning (ML)-derived ICP waveform characteristics. There are gaps, however, in understanding how ICP monitor type may bias waveform characteristics used for these predictive tools since differences between external ventricular drain (EVD) and intraparenchymal monitor (IPM)-derived waveforms have not been well accounted for. OBJECTIVES: We sought to develop a proof-of-concept ML model differentiating ICP waveforms originating from an EVD or IPM. DESIGN, SETTING, AND PARTICIPANTS: We examined raw ICP waveform data from the ICU physiology cohort within the prospective Transforming Research and Clinical Knowledge in Traumatic Brain Injury multicenter study. MAIN OUTCOMES AND MEASURES: Nested patient-wise five-fold cross-validation and group analysis with bagged decision trees (BDT) and linear discriminant analysis were used for feature selection and fair evaluation. Nine patients were kept as unseen hold-outs for further evaluation. RESULTS: ICP waveform data totaling 14,110 hours were included from 82 patients (EVD, 47; IPM, 26; both, 9). Mean age, Glasgow Coma Scale (GCS) total, and GCS motor score upon admission, as well as the presence and amount of midline shift, were similar between groups. The model mean area under the receiver operating characteristic curve (AU-ROC) exceeded 0.874 across all folds. In additional rigorous cluster-based subgroup analysis, targeted at testing the resilience of models to cross-validation with smaller subsets constructed to develop models in one confounder set and test them in another subset, AU-ROC exceeded 0.811. In a similar analysis using propensity score-based rather than cluster-based subgroup analysis, the mean AU-ROC exceeded 0.827. Of 842 extracted ICP features, 62 were invariant within every analysis, representing the most accurate and robust differences between ICP monitor types. For the nine patient hold-outs, an AU-ROC of 0.826 was obtained using BDT. CONCLUSIONS AND RELEVANCE: The developed proof-of-concept ML model identified differences in EVD- and IPM-derived ICP signals, which can provide missing contextual data for large-scale retrospective datasets, prevent bias in computational models that ingest ICP data indiscriminately, and control for confounding using our model's output as a propensity score by to adjust for the monitoring method that was clinically indicated. Furthermore, the invariant features may be leveraged as ICP features for anomaly detection.

Social Determinants of Health and Limitation of Life-Sustaining Therapy in Neurocritical Care: A CHoRUS Pilot Project.

BACKGROUND: Social determinants of health (SDOH) have been linked to neurocritical care outcomes. We sought to examine the extent to which SDOH explain differences in decisions regarding life-sustaining therapy, a key outcome determinant. We specifically investigated the association of a patient's home geography, individual-level SDOH, and neighborhood-level SDOH with subsequent early limitation of life-sustaining therapy (eLLST) and early withdrawal of life-sustaining therapy (eWLST), adjusting for admission severity. METHODS: We developed unique methods within the Bridge to Artificial Intelligence for Clinical Care (Bridge2AI for Clinical Care) Collaborative Hospital Repository Uniting Standards for Equitable Artificial Intelligence (CHoRUS) program to extract individual-level SDOH from electronic health records and neighborhood-level SDOH from privacy-preserving geomapping. We piloted these methods to a 7 years retrospective cohort of consecutive neuroscience intensive care unit admissions (2016-2022) at two large academic medical centers within an eastern Massachusetts health care system, examining associations between home census tract and subsequent occurrence of eLLST and eWLST. We matched contextual neighborhood-level SDOH information to each census tract using public data sets, quantifying Social Vulnerability Index overall scores and subscores. We examined the association of individual-level SDOH and neighborhood-level SDOH with subsequent eLLST and eWLST through geographic, logistic, and machine learning models, adjusting for admission severity using admission Glasgow Coma Scale scores and disorders of consciousness grades. RESULTS: Among 20,660 neuroscience intensive care unit admissions (18,780 unique patients), eLLST and eWLST varied geographically and were independently associated with individual-level SDOH and neighborhood-level SDOH across diagnoses. Individual-level SDOH factors (age, marital status, and race) were strongly associated with eLLST, predicting eLLST more strongly than admission severity. Individual-level SDOH were more strongly predictive of eLLST than neighborhood-level SDOH. CONCLUSIONS: Across diagnoses, eLLST varied by home geography and was predicted by individual-level SDOH and neighborhood-level SDOH more so than by admission severity. Structured shared decision-making tools may therefore represent tools for health equity. Additionally, these findings provide a major warning: prognostic and artificial intelligence models seeking to predict outcomes such as mortality or emergence from disorders of consciousness may be encoded with self-fulfilling biases of geography and demographics.

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.

Increased task-relevant fMRI responsiveness in comatose cardiac arrest patients is associated with improved neurologic outcomes.

Early prediction of the recovery of consciousness in comatose cardiac arrest patients remains challenging. We prospectively studied task-relevant fMRI responses in 19 comatose cardiac arrest patients and five healthy controls to assess the fMRI's utility for neuroprognostication. Tasks involved instrumental music listening, forward and backward language listening, and motor imagery. Task-specific reference images were created from group-level fMRI responses from the healthy controls. Dice scores measured the overlap of individual subject-level fMRI responses with the reference images. Task-relevant responsiveness index (Rindex) was calculated as the maximum Dice score across the four tasks. Correlation analyses showed that increased Dice scores were significantly associated with arousal recovery (P < 0.05) and emergence from the minimally conscious state (EMCS) by one year (P < 0.001) for all tasks except motor imagery. Greater Rindex was significantly correlated with improved arousal recovery (P = 0.002) and consciousness (P = 0.001). For patients who survived to discharge (n = 6), the Rindex's sensitivity was 75% for predicting EMCS (n = 4). Task-based fMRI holds promise for detecting covert consciousness in comatose cardiac arrest patients, but further studies are needed to confirm these findings. Caution is necessary when interpreting the absence of task-relevant fMRI responses as a surrogate for inevitable poor neurological prognosis.

Common Data Elements for Disorders of Consciousness: Recommendations from the Working Group on Physiology and Big Data.

BACKGROUND: The implementation of multimodality monitoring in the clinical management of patients with disorders of consciousness (DoC) results in physiological measurements that can be collected in a continuous and regular fashion or even at waveform resolution. Such data are considered part of the "Big Data" available in intensive care units and are potentially suitable for health care-focused artificial intelligence research. Despite the richness in content of the physiological measurements, and the clinical implications shown by derived metrics based on those measurements, they have been largely neglected from previous attempts in harmonizing data collection and standardizing reporting of results as part of common data elements (CDEs) efforts. CDEs aim to provide a framework for unifying data in clinical research and help in implementing a systematic approach that can facilitate reliable comparison of results from clinical studies in DoC as well in international research collaborations. METHODS: To address this need, the Neurocritical Care Society's Curing Coma Campaign convened a multidisciplinary panel of DoC "Physiology and Big Data" experts to propose CDEs for data collection and reporting in this field. RESULTS: We report the recommendations of this CDE development panel and disseminate CDEs to be used in physiologic and big data studies of patients with DoC. CONCLUSIONS: These CDEs will support progress in the field of DoC physiologic and big data and facilitate international collaboration.

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.

Practice Standards for the Use of Multimodality Neuromonitoring: A Delphi Consensus Process.

OBJECTIVES: To address areas in which there is no consensus for the technologies, effort, and training necessary to integrate and interpret information from multimodality neuromonitoring (MNM). DESIGN: A three-round Delphi consensus process. SETTING: Electronic surveys and virtual meeting. SUBJECTS: Participants with broad MNM expertise from adult and pediatric intensive care backgrounds. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Two rounds of surveys were completed followed by a virtual meeting to resolve areas without consensus and a final survey to conclude the Delphi process. With 35 participants consensus was achieved on 49% statements concerning MNM. Neurologic impairment and the potential for MNM to guide management were important clinical considerations. Experts reached consensus for the use of MNM-both invasive and noninvasive-for patients in coma with traumatic brain injury, aneurysmal subarachnoid hemorrhage, and intracranial hemorrhage. There was consensus that effort to integrate and interpret MNM requires time independent of daily clinical duties, along with specific skills and expertise. Consensus was reached that training and educational platforms are necessary to develop this expertise and to provide clinical correlation. CONCLUSIONS: We provide expert consensus in the clinical considerations, minimum necessary technologies, implementation, and training/education to provide practice standards for the use of MNM to individualize clinical care.

Desirability of Outcome Ranking for Status Epilepticus: A Benefit-Risk Approach to Design and Analyses of Clinical SE Trials.

Most clinical trials of treatment efficacy evaluate benefits and harms separately. Investigators generally rate the primary outcome of a trial with a binary outcome measure and consider harms separately as adverse events. This approach fails to recognize finer gradations of patient response, correlations between benefits and harms, and the overall effects on individual patients. For example, in status epilepticus trials, efficacy is often defined as the absence of clinically apparent seizures with recovery of consciousness. Such an efficacy outcome fails to recognize that some causes of status epilepticus, such as subarachnoid hemorrhage or stroke, may not be accompanied by return of consciousness, and the need to intubate a patient may be classified as treatment failure even if status was successfully terminated. The Desirability of Outcome Ranking (DOOR) method uses a different approach. The DOOR method involves comparing the experiences of trial participants in different treatment arms by the desirability of the overall patient outcome. Using status epilepticus treatment as an example, a patient who experiences successful termination of status epilepticus but with major side effects would have a less desirable outcome than a patient with treatment success and minor side effects, who in turn would have a less desirable outcome than a patient with treatment success but no side effects. This is a patient-centered approach because it considers treatment efficacy in the context of the costs borne by the patient, for example, toxicity in achieving efficacy. Thus, DOOR considers both the benefits and harms to individual patients in assessing the outcome of a clinical trial. In this article, we present the rationale for the use of DOOR, the issues involved in the development of and statistical analyses of an ordinal outcome, and an example of the potential application of the DOOR method to a clinical trial of convulsive status epilepticus.

Practice-Pattern Variation in Sedation of Neurotrauma Patients in the Intensive Care Unit: An International Survey.

Background: Analgo-sedation plays an important role during intensive care management of traumatic brain injury (TBI) patients, however, limited evidence is available to guide practice. We sought to quantify practice-pattern variation in neurotrauma sedation management, surveying an international sample of providers. Methods: An electronic survey consisting of 56 questions was distributed internationally to neurocritical care providers utilizing the Research Electronic Data Capture platform. Descriptive statistics were used to quantitatively describe and summarize the responses. Results: Ninety-five providers from 37 countries responded. 56.8% were attending physicians with primary medical training most commonly in intensive care medicine (68.4%) and anesthesiology (26.3%). Institutional sedation guidelines for TBI patients were available in 43.2%. Most common sedative agents for induction and maintenance, respectively, were propofol (87.5% and 88.4%), opioids (60.2% and 70.5%), and benzodiazepines (53.4% and 68.4%). Induction and maintenance sedatives, respectively, are mostly chosen according to provider preference (68.2% and 58.9%) rather than institutional guidelines (26.1% and 35.8%). Sedation duration for patients with intracranial hypertension ranged from 24 h to 14 days. Neurological wake-up testing (NWT) was routinely performed in 70.5%. The most common NWT frequency was every 24 h (47.8%), although 20.8% performed NWT at least every 2 h. Richmond Agitation and Sedation Scale targets varied from deep sedation (34.7%) to alert and calm (17.9%). Conclusions: Among critically ill TBI patients, sedation management follows provider preference rather than institutional sedation guidelines. Wide practice-pattern variation exists for the type, duration, and target of sedative management and NWT performance. Future comparative effectiveness research investigating these differences may help optimize sedation strategies to promote recovery.

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.

Development of Expert-Level Classification of Seizures and Rhythmic and Periodic Patterns During EEG Interpretation.

BACKGROUND AND OBJECTIVES: Seizures (SZs) and other SZ-like patterns of brain activity can harm the brain and contribute to in-hospital death, particularly when prolonged. However, experts qualified to interpret EEG data are scarce. Prior attempts to automate this task have been limited by small or inadequately labeled samples and have not convincingly demonstrated generalizable expert-level performance. There exists a critical unmet need for an automated method to classify SZs and other SZ-like events with expert-level reliability. This study was conducted to develop and validate a computer algorithm that matches the reliability and accuracy of experts in identifying SZs and SZ-like events, known as "ictal-interictal-injury continuum" (IIIC) patterns on EEG, including SZs, lateralized and generalized periodic discharges (LPD, GPD), and lateralized and generalized rhythmic delta activity (LRDA, GRDA), and in differentiating these patterns from non-IIIC patterns. METHODS: We used 6,095 scalp EEGs from 2,711 patients with and without IIIC events to train a deep neural network, SPaRCNet, to perform IIIC event classification. Independent training and test data sets were generated from 50,697 EEG segments, independently annotated by 20 fellowship-trained neurophysiologists. We assessed whether SPaRCNet performs at or above the sensitivity, specificity, precision, and calibration of fellowship-trained neurophysiologists for identifying IIIC events. Statistical performance was assessed by the calibration index and by the percentage of experts whose operating points were below the model's receiver operating characteristic curves (ROCs) and precision recall curves (PRCs) for the 6 pattern classes. RESULTS: SPaRCNet matches or exceeds most experts in classifying IIIC events based on both calibration and discrimination metrics. For SZ, LPD, GPD, LRDA, GRDA, and "other" classes, SPaRCNet exceeds the following percentages of 20 experts-ROC: 45%, 20%, 50%, 75%, 55%, and 40%; PRC: 50%, 35%, 50%, 90%, 70%, and 45%; and calibration: 95%, 100%, 95%, 100%, 100%, and 80%, respectively. DISCUSSION: SPaRCNet is the first algorithm to match expert performance in detecting SZs and other SZ-like events in a representative sample of EEGs. With further development, SPaRCNet may thus be a valuable tool for an expedited review of EEGs. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that among patients with epilepsy or critical illness undergoing EEG monitoring, SPaRCNet can differentiate (IIIC) patterns from non-IIIC events and expert neurophysiologists.

Why ketamine.

We present the rationale for testing ketamine as an add-on therapy for treating benzodiazepine refractory (established) status epilepticus. In animal studies, ketamine terminates benzodiazepine refractory status epilepticus by interfering with the pathophysiological mechanisms and is a neuroprotectant. Ketamine does not suppress respiration when used for sedation and anesthesia. A Series of reports suggest that ketamine can help terminate refractory and super refractory status epilepticus. We propose to use 1 or 3 mg/Kg ketamine intravenously based on animal-to-human conversion and pharmacokinetic studies. This paper was presented at the 8th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures held in September 2022.

Interrater Reliability of Expert Electroencephalographers Identifying Seizures and Rhythmic and Periodic Patterns in EEGs.

BACKGROUND AND OBJECTIVES: The validity of brain monitoring using electroencephalography (EEG), particularly to guide care in patients with acute or critical illness, requires that experts can reliably identify seizures and other potentially harmful rhythmic and periodic brain activity, collectively referred to as "ictal-interictal-injury continuum" (IIIC). Previous interrater reliability (IRR) studies are limited by small samples and selection bias. This study was conducted to assess the reliability of experts in identifying IIIC. METHODS: This prospective analysis included 30 experts with subspecialty clinical neurophysiology training from 18 institutions. Experts independently scored varying numbers of ten-second EEG segments as "seizure (SZ)," "lateralized periodic discharges (LPDs)," "generalized periodic discharges (GPDs)," "lateralized rhythmic delta activity (LRDA)," "generalized rhythmic delta activity (GRDA)," or "other." EEGs were performed for clinical indications at Massachusetts General Hospital between 2006 and 2020. Primary outcome measures were pairwise IRR (average percent agreement [PA] between pairs of experts) and majority IRR (average PA with group consensus) for each class and beyond chance agreement (κ). Secondary outcomes were calibration of expert scoring to group consensus, and latent trait analysis to investigate contributions of bias and noise to scoring variability. RESULTS: Among 2,711 EEGs, 49% were from women, and the median (IQR) age was 55 (41) years. In total, experts scored 50,697 EEG segments; the median [range] number scored by each expert was 6,287.5 [1,002, 45,267]. Overall pairwise IRR was moderate (PA 52%, κ 42%), and majority IRR was substantial (PA 65%, κ 61%). Noise-bias analysis demonstrated that a single underlying receiver operating curve can account for most variation in experts' false-positive vs true-positive characteristics (median [range] of variance explained ([Formula: see text]): 95 [93, 98]%) and for most variation in experts' precision vs sensitivity characteristics ([Formula: see text]: 75 [59, 89]%). Thus, variation between experts is mostly attributable not to differences in expertise but rather to variation in decision thresholds. DISCUSSION: Our results provide precise estimates of expert reliability from a large and diverse sample and a parsimonious theory to explain the origin of disagreements between experts. The results also establish a standard for how well an automated IIIC classifier must perform to match experts. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that an independent expert review reliably identifies ictal-interictal injury continuum patterns on EEG compared with expert consensus.

Physiological Monitoring in Patients with Acute Brain Injury: A Multimodal Approach.

Neurocritical care management of acute brain injury (ABI) is focused on identification, prevention, and management of secondary brain injury (SBI). Physiologic monitoring of the brain and other organ systems has a role to predict patient recovery or deterioration, guide individualized therapeutic interventions, and measure response to treatment, with the goal of improving patient outcomes. In this review, we detail how specific physiologic markers of brain injury and neuromonitoring tools are integrated and used in ABI patients to develop therapeutic approaches to prevent SBI.

Neurocritical Care Performance Measures Derived from Electronic Health Record Data are Feasible and Reveal Site-Specific Variation: A CHoRUS Pilot Project.

BACKGROUND: We evaluated the feasibility and discriminability of recently proposed Clinical Performance Measures for Neurocritical Care (Neurocritical Care Society) and Quality Indicators for Traumatic Brain Injury (Collaborative European NeuroTrauma Effectiveness Research in TBI; CENTER-TBI) extracted from electronic health record (EHR) flowsheet data. METHODS: At three centers within the Collaborative Hospital Repository Uniting Standards (CHoRUS) for Equitable AI consortium, we examined consecutive neurocritical care admissions exceeding 24 h (03/2015-02/2020) and evaluated the feasibility, discriminability, and site-specific variation of five clinical performance measures and quality indicators: (1) intracranial pressure (ICP) monitoring (ICPM) within 24 h when indicated, (2) ICPM latency when initiated within 24 h, (3) frequency of nurse-documented neurologic assessments, (4) intermittent pneumatic compression device (IPCd) initiation within 24 h, and (5) latency to IPCd application. We additionally explored associations between delayed IPCd initiation and codes for venous thromboembolism documented using the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) system. Median (interquartile range) statistics are reported. Kruskal-Wallis tests were measured for differences across centers, and Dunn statistics were reported for between-center differences. RESULTS: A total of 14,985 admissions met inclusion criteria. ICPM was documented in 1514 (10.1%), neurologic assessments in 14,635 (91.1%), and IPCd application in 14,175 (88.5%). ICPM began within 24 h for 1267 (83.7%), with site-specific latency differences among sites 1-3, respectively, (0.54 h [2.82], 0.58 h [1.68], and 2.36 h [4.60]; p < 0.001). The frequency of nurse-documented neurologic assessments also varied by site (17.4 per day [5.97], 8.4 per day [3.12], and 15.3 per day [8.34]; p < 0.001) and diurnally (6.90 per day during daytime hours vs. 5.67 per day at night, p < 0.001). IPCds were applied within 24 h for 12,863 (90.7%) patients meeting clinical eligibility (excluding those with EHR documentation of limiting injuries, actively documented as ambulating, or refusing prophylaxis). In-hospital venous thromboembolism varied by site (1.23%, 1.55%, and 5.18%; p < 0.001) and was associated with increased IPCd latency (overall, 1.02 h [10.4] vs. 0.97 h [5.98], p = 0.479; site 1, 2.25 h [10.27] vs. 1.82 h [7.39], p = 0.713; site 2, 1.38 h [5.90] vs. 0.80 h [0.53], p = 0.216; site 3, 0.40 h [16.3] vs. 0.35 h [11.5], p = 0.036). CONCLUSIONS: Electronic health record-derived reporting of neurocritical care performance measures is feasible and demonstrates site-specific variation. Future efforts should examine whether performance or documentation drives these measures, what outcomes are associated with performance, and whether EHR-derived measures of performance measures and quality indicators are modifiable.