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.

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.

Post-Intensive Care Syndrome in Neurocritical Care Patients.

Post-intensive care syndrome (PICS) refers to unintended consequences of critical care that manifest as new or worsening impairments in physical functioning, cognitive ability, or mental health. As intensive care unit (ICU) survival continues to improve, PICS is becoming increasingly recognized as a public health problem. Studies that focus on PICS have typically excluded patients with acute brain injuries and chronic neurodegenerative problems. However, patients who require neurocritical care undoubtedly suffer from impairments that overlap substantially with those encompassed by PICS. A major challenge is to distinguish between impairments related to brain injury and those that occur as a consequence of critical care. The general principles for the prevention and management of PICS and multidomain impairments in patients with moderate and severe neurological injuries are similar including the ICU liberation bundle, multidisciplinary team-based care throughout the continuum of care, and increasing awareness regarding the challenges of critical care survivorship among patients, families, and multidisciplinary team members. An extension of this concept, PICS-Family (PICS-F) refers to the mental health consequences of the intensive care experience for families and loved ones of ICU survivors. A dyadic approach to ICU survivorship with an emphasis on recognizing families and caregivers that may be at risk of developing PICS-F after neurocritical care illness can help improve outcomes for ICU survivors. In this review, we will summarize our current understanding of PICS and PICS-F, emerging literature on PICS in severe acute brain injury, strategies for preventing and treating PICS, and share our recommendations for future directions.

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.

Pharmacokinetics of Levetiracetam Seizure Prophylaxis in Severe Traumatic Brain Injury.

BACKGROUND: Drug pharmacokinetics (PK) are altered in neurocritically ill patients, and optimal levetiracetam dosing for seizure prophylaxis is unknown. OBJECTIVE: This study evaluates levetiracetam PK in critically ill patients with severe traumatic brain injury (sTBI) receiving intravenous levetiracetam 1000 mg every 8 (LEV8) to 12 (LEV12) hours for seizure prophylaxis. METHODS: This prospective, open-label study was conducted at a level 1 trauma, academic, quaternary care center. Patients with sTBI receiving seizure prophylaxis with LEV8 or LEV12 were eligible for enrollment. Five sequential, steady-state, postdose serum levetiracetam concentrations were obtained. Non-compartmental analysis (NCA) and compartmental approaches were employed for estimating pharmacokinetic parameters and projecting steady-state trough concentrations. Pharmacokinetic parameters were compared between LEV8 and LEV12 patients. Monte Carlo simulations (MCS) were performed to determine probability of target trough attainment (PTA) of 6 to 20 mg/L. A secondary analysis evaluated PTA for weight-tiered levetiracetam dosing. RESULTS: Ten male patients (5 LEV8; 5 LEV12) were included. The NCA-based systemic clearance and elimination half-life were 5.3 ± 1.2 L/h and 4.8 ± 0.64 hours. A one-compartment model provided a higher steady-state trough concentration for the LEV8 group compared with the LEV12 group (13.7 ± 4.3 mg/L vs 6.3 ± 1.7 mg/L; P = 0.008). Monte Carlo simulations predicted regimens of 500 mg every 6 hours, 1000 mg every 8 hours, and 2000 mg every 12 hours achieved therapeutic target attainment. Weight-tiered dosing regimens achieved therapeutic target attainment using a 75 kg breakpoint. CONCLUSION AND RELEVANCE: Neurocritically ill patients exhibit rapid levetiracetam clearance resulting in a short elimination half-life. Findings of this study suggest regimens of levetiracetam 500 mg every 6 hours, 1000 mg every 8 hours, or 2000 mg every 12 hours may be required for optimal therapeutic target attainment. Patient weight of 75 kg may serve as a breakpoint for weight-guided dosing to optimize levetiracetam therapeutic target attainment for seizure prophylaxis.

WindowSHAP: An efficient framework for explaining time-series classifiers based on Shapley values.

Unpacking and comprehending how black-box machine learning algorithms (such as deep learning models) make decisions has been a persistent challenge for researchers and end-users. Explaining time-series predictive models is useful for clinical applications with high stakes to understand the behavior of prediction models, e.g., to determine how different variables and time points influence the clinical outcome. However, existing approaches to explain such models are frequently unique to architectures and data where the features do not have a time-varying component. In this paper, we introduce WindowSHAP, a model-agnostic framework for explaining time-series classifiers using Shapley values. We intend for WindowSHAP to mitigate the computational complexity of calculating Shapley values for long time-series data as well as improve the quality of explanations. WindowSHAP is based on partitioning a sequence into time windows. Under this framework, we present three distinct algorithms of Stationary, Sliding and Dynamic WindowSHAP, each evaluated against baseline approaches, KernelSHAP and TimeSHAP, using perturbation and sequence analyses metrics. We applied our framework to clinical time-series data from both a specialized clinical domain (Traumatic Brain Injury - TBI) as well as a broad clinical domain (critical care medicine). The experimental results demonstrate that, based on the two quantitative metrics, our framework is superior at explaining clinical time-series classifiers, while also reducing the complexity of computations. We show that for time-series data with 120 time steps (hours), merging 10 adjacent time points can reduce the CPU time of WindowSHAP by 80 % compared to KernelSHAP. We also show that our Dynamic WindowSHAP algorithm focuses more on the most important time steps and provides more understandable explanations. As a result, WindowSHAP not only accelerates the calculation of Shapley values for time-series data, but also delivers more understandable explanations with higher quality.

A self-supervised learning-based approach to clustering multivariate time-series data with missing values (SLAC-Time): An application to TBI phenotyping.

Self-supervised learning approaches provide a promising direction for clustering multivariate time-series data. However, real-world time-series data often include missing values, and the existing approaches require imputing missing values before clustering, which may cause extensive computations and noise and result in invalid interpretations. To address these challenges, we present a Self-supervised Learning-based Approach to Clustering multivariate Time-series data with missing values (SLAC-Time). SLAC-Time is a Transformer-based clustering method that uses time-series forecasting as a proxy task for leveraging unlabeled data and learning more robust time-series representations. This method jointly learns the neural network parameters and the cluster assignments of the learned representations. It iteratively clusters the learned representations with the K-means method and then utilizes the subsequent cluster assignments as pseudo-labels to update the model parameters. To evaluate our proposed approach, we applied it to clustering and phenotyping Traumatic Brain Injury (TBI) patients in the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study. Clinical data associated with TBI patients are often measured over time and represented as time-series variables characterized by missing values and irregular time intervals. Our experiments demonstrate that SLAC-Time outperforms the baseline K-means clustering algorithm in terms of silhouette coefficient, Calinski Harabasz index, Dunn index, and Davies Bouldin index. We identified three TBI phenotypes that are distinct from one another in terms of clinically significant variables as well as clinical outcomes, including the Extended Glasgow Outcome Scale (GOSE) score, Intensive Care Unit (ICU) length of stay, and mortality rate. The experiments show that the TBI phenotypes identified by SLAC-Time can be potentially used for developing targeted clinical trials and therapeutic strategies.

Common Data Elements for Disorders of Consciousness: Recommendations from the Working Group on Hospital Course, Confounders, and Medications.

The convergence of an interdisciplinary team of neurocritical care specialists to organize the Curing Coma Campaign is the first effort of its kind to coordinate national and international research efforts aimed at a deeper understanding of disorders of consciousness (DoC). This process of understanding includes translational research from bench to bedside, descriptions of systems of care delivery, diagnosis, treatment, rehabilitation, and ethical frameworks. The description and measurement of varying confounding factors related to hospital care was thought to be critical in furthering meaningful research in patients with DoC. Interdisciplinary hospital care is inherently varied across geographical areas as well as community and academic medical centers. Access to monitoring technologies, specialist consultation (medical, nursing, pharmacy, respiratory, and rehabilitation), staffing resources, specialty intensive and acute care units, specialty medications and specific surgical, diagnostic and interventional procedures, and imaging is variable, and the impact on patient outcome in terms of DoC is largely unknown. The heterogeneity of causes in DoC is the source of some expected variability in care and treatment of patients, which necessitated the development of a common nomenclature and set of data elements for meaningful measurement across studies. Guideline adherence in hemorrhagic stroke and severe traumatic brain injury may also be variable due to moderate or low levels of evidence for many recommendations. This article outlines the process of the development of common data elements for hospital course, confounders, and medications to streamline definitions and variables to collect for clinical studies of DoC.

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.

Harmonization of Physiological Data in Neurocritical Care: Challenges and a Path Forward.

Continuous multimodal monitoring in neurocritical care provides valuable insights into the dynamics of the injured brain. Unfortunately, the "readiness" of this data for robust artificial intelligence (AI) and machine learning (ML) applications is low and presents a significant barrier for advancement. Harmonization standards and tools to implement those standards are key to overcoming existing barriers. Consensus in our professional community is essential for success.

Seizures and Cognitive Outcome After Traumatic Brain Injury: A Post Hoc Analysis.

INTRODUCTION: Seizures and abnormal periodic or rhythmic patterns are observed on continuous electroencephalography monitoring (cEEG) in up to half of patients hospitalized with moderate to severe traumatic brain injury (TBI). We aimed to determine the impact of seizures and abnormal periodic or rhythmic patterns on cognitive outcome 3 months following moderate to severe TBI. METHODS: This was a post hoc analysis of the multicenter randomized controlled phase 2 INTREPID2566 clinical trial conducted from 2010 to 2016 across 20 United States Level I trauma centers. Patients with nonpenetrating TBI and postresuscitation Glasgow Coma Scale scores 4-12 were included. Bedside cEEG was initiated per protocol on admission to intensive care, and the burden of ictal-interictal continuum (IIC) patterns, including seizures, was quantified. A summary global cognition score at 3 months following injury was used as the primary outcome. RESULTS: 142 patients (age mean + / - standard deviation 32 + / - 13 years; 131 [92%] men) survived with a mean global cognition score of 81 + / - 15; nearly one third were considered to have poor functional outcome. 89 of 142 (63%) patients underwent cEEG, of whom 13 of 89 (15%) had severe IIC patterns. The quantitative burden of IIC patterns correlated inversely with the global cognition score (r = - 0.57; p = 0.04). In multiple variable analysis, the log-transformed burden of severe IIC patterns was independently associated with the global cognition score after controlling for demographics, premorbid estimated intelligence, injury severity, sedatives, and antiepileptic drugs (odds ratio 0.73, 95% confidence interval 0.60-0.88; p = 0.002). CONCLUSIONS: The burden of seizures and abnormal periodic or rhythmic patterns was independently associated with worse cognition at 3 months following TBI. Their impact on longer-term cognitive endpoints and the potential benefits of seizure detection and treatment in this population warrant prospective study.

The Relationship Between Seizures and Spreading Depolarizations in Patients with Severe Traumatic Brain Injury.

BACKGROUND: Both seizures and spreading depolarizations (SDs) are commonly detected using electrocorticography (ECoG) after severe traumatic brain injury (TBI). A close relationship between seizures and SDs has been described, but the implications of detecting either or both remain unclear. We sought to characterize the relationship between these two phenomena and their clinical significance. METHODS: We performed a post hoc analysis of a prospective observational clinical study of patients with severe TBI requiring neurosurgery at five academic neurotrauma centers. A subdural electrode array was placed intraoperatively and ECoG was recorded during intensive care. SDs, seizures, and high-frequency background characteristics were quantified offline using published standards and terminology. The primary outcome was the Glasgow Outcome Scale-Extended score at 6 months post injury. RESULTS: There were 138 patients with valid ECoG recordings; the mean age was 47 ± 19 years, and 104 (75%) were men. Overall, 2,219 ECoG-detected seizures occurred in 38 of 138 (28%) patients in a bimodal pattern, with peak incidences at 1.7-1.8 days and 3.8-4.0 days post injury. Seizures detected on scalp electroencephalography (EEG) were diagnosed by standard clinical care in only 18 of 138 (13%). Of 15 patients with ECoG-detected seizures and contemporaneous scalp EEG, seven (47%) had no definite scalp EEG correlate. ECoG-detected seizures were significantly associated with the severity and number of SDs, which occurred in 83 of 138 (60%) of patients. Temporal interactions were observed in 17 of 24 (70.8%) patients with both ECoG-detected seizures and SDs. After controlling for known prognostic covariates and the presence of SDs, seizures detected on either ECoG or scalp EEG did not have an independent association with 6-month functional outcome but portended worse outcome among those with clustered or isoelectric SDs. CONCLUSIONS: In patients with severe TBI requiring neurosurgery, seizures were half as common as SDs. Seizures would have gone undetected without ECoG monitoring in 20% of patients. Although seizures alone did not influence 6-month functional outcomes in this cohort, they were independently associated with electrographic worsening and a lack of motor improvement following surgery. Temporal interactions between ECoG-detected seizures and SDs were common and held prognostic implications. Together, seizures and SDs may occur along a dynamic continuum of factors critical to the development of secondary brain injury. ECoG provides information integral to the clinical management of patients with TBI.

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.

Association of Vasopressor Choice with Clinical and Functional Outcomes Following Moderate to Severe Traumatic Brain Injury: A TRACK-TBI Study.

BACKGROUND: Early hypotension following moderate to severe traumatic brain injury (TBI) is associated with increased mortality and poor long-term outcomes. Current guidelines suggest the use of intravenous vasopressors to support blood pressure following TBI; however, guidelines do not specify vasopressor type, resulting in variation in clinical practice. Minimal data are available to guide clinicians on optimal early vasopressor choice to support blood pressure following TBI. Therefore, we conducted a multicenter study to examine initial vasopressor choice for the support of blood pressure following TBI and its association with clinical and functional outcomes after injury. METHODS: We conducted a retrospective cohort study of patients enrolled in the transforming research and clinical knowledge in traumatic brain injury (TRACK-TBI) study, an 18-center prospective cohort study of patients with TBI evaluated in participating level I trauma centers. We examined adults with moderate to severe TBI (defined as Glasgow Coma Scale score < 13) who were admitted to the intensive care unit and received an intravenous vasopressor within 48 h of admission. The primary exposure was initial vasopressor choice (phenylephrine versus norepinephrine), and the primary outcome was 6-month Glasgow Outcomes Scale Extended (GOSE), with the following secondary outcomes: length of hospital stay, length of intensive care unit stay, in-hospital mortality, new requirement for dialysis, and 6-month Disability Rating Scale. Regression analysis was used to assess differences in outcomes between patients exposed to norepinephrine versus phenylephrine, with propensity weighting to address selection bias due to the nonrandom allocation of the treatment groups and patient dropout. RESULTS: The final study sample included 156 patients, of whom 79 (51%) received norepinephrine, 69 (44%) received phenylephrine, and 8 (5%) received an alternate drug as their initial vasopressor. 121 (77%) of patients were men, with a mean age of 43.1 years. Of patients receiving norepinephrine as their initial vasopressor, 32% had a favorable outcome (GOSE 5-8), whereas 40% of patients receiving phenylephrine as their initial vasopressor had a favorable outcome. Compared with phenylephrine, exposure to norepinephrine was not significantly associated with improved 6-month GOSE (weighted odds ratio 1.40, 95% confidence interval 0.66-2.96, p = 0.37) or any secondary outcome. CONCLUSIONS: The majority of patients with moderate to severe TBI received either phenylephrine or norepinephrine as first-line agents for blood pressure support following brain injury. Initial choice of norepinephrine, compared with phenylephrine, was not associated with improved clinical or functional outcomes.

Proceedings of the Second Curing Coma Campaign NIH Symposium: Challenging the Future of Research for Coma and Disorders of Consciousness.

This proceedings article presents actionable research targets on the basis of the presentations and discussions at the 2nd Curing Coma National Institutes of Health (NIH) symposium held from May 3 to May 5, 2021. Here, we summarize the background, research priorities, panel discussions, and deliverables discussed during the symposium across six major domains related to disorders of consciousness. The six domains include (1) Biology of Coma, (2) Coma Database, (3) Neuroprognostication, (4) Care of Comatose Patients, (5) Early Clinical Trials, and (6) Long-term Recovery. Following the 1st Curing Coma NIH virtual symposium held on September 9 to September 10, 2020, six workgroups, each consisting of field experts in respective domains, were formed and tasked with identifying gaps and developing key priorities and deliverables to advance the mission of the Curing Coma Campaign. The highly interactive and inspiring presentations and panel discussions during the 3-day virtual NIH symposium identified several action items for the Curing Coma Campaign mission, which we summarize in this article.

Association of Early Multiple Organ Dysfunction With Clinical and Functional Outcomes Over the Year Following Traumatic Brain Injury: A Transforming Research and Clinical Knowledge in Traumatic Brain Injury Study.

OBJECTIVES: Traumatic brain injury is a leading cause of death and disability in the United States. While the impact of early multiple organ dysfunction syndrome has been studied in many critical care paradigms, the clinical impact of early multiple organ dysfunction syndrome in traumatic brain injury is poorly understood. We examined the incidence and impact of early multiple organ dysfunction syndrome on clinical, functional, and disability outcomes over the year following traumatic brain injury. DESIGN: Retrospective cohort study. SETTING: Patients enrolled in the Transforming Clinical Research and Knowledge in Traumatic Brain Injury study, an 18-center prospective cohort study of traumatic brain injury patients evaluated in participating level 1 trauma centers. SUBJECTS: Adult (age > 17 yr) patients with moderate-severe traumatic brain injury (Glasgow Coma Scale < 13). We excluded patients with major extracranial injury (Abbreviated Injury Scale score ≥ 3). INTERVENTIONS: Development of early multiple organ dysfunction syndrome, defined as a maximum modified Sequential Organ Failure Assessment score greater than 7 during the initial 72 hours following admission. MEASUREMENTS AND MAIN RESULTS: The main outcomes were: hospital mortality, length of stay, 6-month functional and disability domains (Glasgow Outcome Scale-Extended and Disability Rating Scale), and 1-year mortality. Secondary outcomes included: ICU length of stay, 3-month Glasgow Outcome Scale-Extended, 3-month Disability Rating Scale, 1-year Glasgow Outcome Scale-Extended, and 1-year Disability Rating Scale. We examined 373 subjects with moderate-severe traumatic brain injury. The mean (sd) Glasgow Coma Scale in the emergency department was 5.8 (3.2), with 280 subjects (75%) classified as severe traumatic brain injury (Glasgow Coma Scale 3-8). Among subjects with moderate-severe traumatic brain injury, 252 (68%) developed early multiple organ dysfunction syndrome. Subjects that developed early multiple organ dysfunction syndrome had a 75% decreased odds of a favorable outcome (Glasgow Outcome Scale-Extended 5-8) at 6 months (adjusted odds ratio, 0.25; 95% CI, 0.12-0.51) and increased disability (higher Disability Rating Scale score) at 6 months (adjusted mean difference, 2.04; 95% CI, 0.92-3.17). Subjects that developed early multiple organ dysfunction syndrome experienced an increased hospital length of stay (adjusted mean difference, 11.4 d; 95% CI, 7.1-15.8), with a nonsignificantly decreased survival to hospital discharge (odds ratio, 0.47; 95% CI, 0.18-1.2). CONCLUSIONS: Early multiple organ dysfunction following moderate-severe traumatic brain injury is common and independently impacts multiple domains (mortality, function, and disability) over the year following injury. Further research is necessary to understand underlying mechanisms, improve early recognition, and optimize management strategies.

Efficacy of Noninvasive Technologies in Triaging Traumatic Brain Injury and Correlating With Intracranial Pressure: A Prospective Study.

BACKGROUND: There is interest in methods of measuring noninvasive intracranial pressure (ICP), including pupillometry, ultrasonographic transcranial Doppler (TCD), and optic nerve sheath diameter (ONSD), for diagnosing traumatic brain injury (TBI) in limited resource environments. Whether these technologies have diagnostic agreement is unknown. We hypothesized that ONSD, pupillometry, and TCD could both distinguish severe TBI and correlate with ICP. METHODS: A prospective study of 135 patients was conducted at a level 1 trauma center. Four test groups were established: nontrauma patients with ICP monitoring, trauma patients without TBI, trauma patients with mild TBI, and trauma patients with severe TBI with ICP monitoring. All patients underwent daily measurements of ONSD, pupillometry, and TCD with both CX50 Sonosite and the Spencer ST3 Yi Pencil probe. RESULTS: ONSD differed significantly in patients with severe TBI compared with patients with mild and no TBI, but did not correlate with ICP. Pupillometric constriction velocity, dilation velocity, and percent change in pupil diameter were significantly different in patients with severe TBI, but also did not correlate with ICP. TCD did not differ among TBI severities, but middle cerebral artery peak systolic velocity, middle cerebral artery flow velocity, and carotid flow velocity correlated with ICP. CONCLUSIONS: This is a novel study of four noninvasive tests to screen for severity of TBI and measure ICP. Our analysis indicates that no single device can do both. However, ONSD and pupillometry may be used as a supplementary screening tool for severe TBI, whereas TCD could be used to estimate and follow ICP in patients with severe TBI.

Challenges and Opportunities in Multimodal Monitoring and Data Analytics in Traumatic Brain Injury.

PURPOSE OF REVIEW: Increasingly sophisticated systems for monitoring the brain have led to an increase in the use of multimodality monitoring (MMM) to detect secondary brain injuries before irreversible damage occurs after brain trauma. This review examines the challenges and opportunities associated with MMM in this population. RECENT FINDINGS: Locally and internationally, the use of MMM varies. Practical challenges include difficulties with data acquisition, curation, and harmonization with other data sources limiting collaboration. However, efforts toward integration of MMM data, advancements in data science, and the availability of cloud-based infrastructures are now affording the opportunity for MMM to advance the care of patients with brain trauma. MMM provides data to guide the precision management of patients with traumatic brain injury in real time. While challenges exist, there are exciting opportunities for MMM to live up to this promise and to drive new insights into the physiology of the brain and beyond.

Antithrombotic regimens and need for critical care interventions among patients with subdural hematomas.

BACKGROUND: Antithrombotic-associated subdural hematomas (SDHs) are increasingly common, and the possibility of clinical deterioration in otherwise stable antithrombotic-associated SDH patients may prompt unnecessary admissions to intensive care units. It is unknown whether all antithrombotic regimens are equally associated with the need for critical care interventions. We sought to compare the frequency of critical care interventions and poor functional outcomes among three cohorts of noncomatose SDH patients: patients on no antithrombotics, patients on anticoagulants, and patients on antiplatelets alone. METHODS: We performed a retrospective cohort study on all noncomatose SDH patients (Glasgow Coma Scale > 12) presenting to an academic health system in 2018. The three groups of patients were compared in terms of clinical course and functional outcome. Multivariable logistic regression was used to determine predictors of need for critical care interventions and poor functional outcome at hospital discharge. RESULTS: There were 281 eligible patients presenting with SDHs in 2018, with 126 (45%) patients on no antithrombotics, 106 (38%) patients on antiplatelet medications alone, and 49 (17%) patients on anticoagulants. Significant predictors of critical care interventions were coagulopathy (OR 5.1, P < 0.001), presence of contusions (OR 3, P = 0.007), midline shift (OR 3.4, P = 0.002), and maximum SDH thickness (OR 2.4, P = 0.002). Significant predictors of poor functional outcome were age (OR 1.8, P < 0.001), admission Glasgow Coma Scale score (OR 0.3, P < 0.001), dementia history (OR 4.2, P = 0.001), and coagulopathy (OR 3.5, P = 0.02). Isolated antiplatelet use was not associated with either critical care interventions or functional outcome. CONCLUSION: Isolated antiplatelet use is not a significant predictor of need for critical care interventions or poor functional outcome among SDH patients and should not be used as a criterion for triage to the intensive care unit.

Development and Evaluation of a Method for Automated Detection of Spreading Depolarizations in the Injured Human Brain.

BACKGROUND: Spreading depolarizations (SDs) occur in some 60% of patients receiving intensive care following severe traumatic brain injury and often occur at a higher incidence following serious subarachnoid hemorrhage and malignant hemisphere stroke (MHS); they are independently associated with worse clinical outcome. Detection of SDs to guide clinical management, as is now being advocated, currently requires continuous and skilled monitoring of the electrocorticogram (ECoG), frequently extending over many days. METHODS: We developed and evaluated in two clinical intensive care units (ICU) a software routine capable of detecting SDs both in real time at the bedside and retrospectively and also capable of displaying patterns of their occurrence with time. We tested this prototype software in 91 data files, each of approximately 24 h, from 18 patients, and the results were compared with those of manual assessment ("ground truth") by an experienced assessor blind to the software outputs. RESULTS: The software successfully detected SDs in real time at the bedside, including in patients with clusters of SDs. Counts of SDs by software (dependent variable) were compared with ground truth by the investigator (independent) using linear regression. The slope of the regression was 0.7855 (95% confidence interval 0.7149-0.8561); a slope value of 1.0 lies outside the 95% confidence interval of the slope, representing significant undersensitivity of 79%. R2 was 0.8415. CONCLUSIONS: Despite significant undersensitivity, there was no additional loss of sensitivity at high SD counts, thus ensuring that dense clusters of depolarizations of particular pathogenic potential can be detected by software and depicted to clinicians in real time and also be archived.