Early Seizure Prophylaxis in Mild and Moderate Traumatic Brain Injury: A Systematic Review and Meta-Analysis.

Importance: Guidelines recommend seizure prophylaxis for early posttraumatic seizures (PTS) after severe traumatic brain injury (TBI). Use of antiseizure medications for early seizure prophylaxis after mild or moderate TBI remains controversial. Objective: To determine the association between seizure prophylaxis and risk reduction for early PTS in mild and moderate TBI. Data Sources: PubMed, Google Scholar, and Web of Science (January 1, 1991, to April 18, 2023) were systematically searched. Study Selection: Observational studies of adult patients presenting to trauma centers in high-income countries with mild (Glasgow Coma Scale [GCS], 13-15) and moderate (GCS, 9-12) TBI comparing rates of early PTS among patients with seizure prophylaxis with those without seizure prophylaxis. Data Extraction and Synthesis: The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) reporting guidelines were used. Two authors independently reviewed all titles and abstracts, and 3 authors reviewed final studies for inclusion. A meta-analysis was performed using a random-effects model with absolute risk reduction. Main Outcome Measures: The main outcome was absolute risk reduction of early PTS, defined as seizures within 7 days of initial injury, in patients with mild or moderate TBI receiving seizure prophylaxis in the first week after injury. A secondary analysis was performed in patients with only mild TBI. Results: A total of 64 full articles were reviewed after screening; 8 studies (including 5637 patients) were included for the mild and moderate TBI analysis, and 5 studies (including 3803 patients) were included for the mild TBI analysis. The absolute risk reduction of seizure prophylaxis for early PTS in mild to moderate TBI (GCS, 9-15) was 0.6% (95% CI, 0.1%-1.2%; P = .02). The absolute risk reduction for mild TBI alone was similar 0.6% (95% CI, 0.01%-1.2%; P = .04). The number needed to treat to prevent 1 seizure was 167 patients. Conclusion and Relevance: Seizure prophylaxis after mild and moderate TBI was associated with a small but statistically significant reduced risk of early posttraumatic seizures after mild and moderate TBI. The small absolute risk reduction and low prevalence of early seizures should be weighed against potential acute risks of antiseizure medications as well as the risk of inappropriate continuation beyond 7 days.

Clinicians' approach to predicting post-cardiac arrest outcomes for patients enrolled in a United States clinical trial.

PURPOSE: Perceived poor prognosis can lead to withdrawal of life-sustaining therapies (WLST) in patients who might otherwise recover. We characterized clinicians' approach to post-arrest prognostication in a multicenter clinical trial. METHODS: Semi-structured interviews were conducted with clinicians who treated a comatose post-cardiac arrest patient enrolled in the Influence of Cooling Duration on Efficacy in Cardiac Arrest Patients (ICECAP) trial (NCT04217551). Two authors independently analyzed each interview using inductive and deductive coding. The clinician reported how they arrived at a prognosis for the specific patient. We summarized the frequency with which clinicians reported using objective diagnostics to formulate their prognosis, and compared the reported approaches to established guidelines. Each respondent provided demographic information and described local neuroprognostication practices. RESULTS: We interviewed 30 clinicians at 19 US hospitals. Most claimed adherence to local hospital neuroprognostication protocols (n = 19). Prognostication led to WLST for perceived poor neurological prognosis in 15/30 patients, of whom most showed inconsistencies with guidelines or trial recommendations, respectively. In 10/15 WLST cases, clinicians reported relying on multimodal testing. A prevalent theme was the use of "clinical gestalt," defined as prognosticating based on a patient's overall appearance or a subjective impression in the absence of objective data. Many clinicians (21/30) reported using clinical gestalt for initial prognostication, with 9/21 expressing high confidence initially. CONCLUSION: Clinicians in our study state they follow neuroprognostication guidelines in general but often do not do so in actual practice. They reported clinical gestalt frequently informed early, highly confident prognostic judgments, and few objective tests changed initial impressions. Subjective prognostication may undermine well-designed trials.

Emotional distress, social support, and functional dependence predict readiness for hospital discharge in a prospective sample of cognitively intact cardiac arrest survivors.

AIM: To inform screening, referral and treatment initiatives, we tested the hypothesis that emotional distress, social support, functional dependence, and cognitive impairment within 72 hours prior to discharge predict readiness for discharge in awake and alert cardiac arrest (CA) survivors. METHODS: This was a secondary analysis of a prospective single-center cohort of CA survivors enrolled between 4/2021 and 9/2022. We quantified emotional distress using the Posttraumatic Stress Disorder Checklist-5 and PROMIS Emotional Distress - Anxiety and Depression Short Forms 4a; perceived social support using the ENRICHD Social Support Inventory; functional dependence using the modified Rankin Scale; and cognitive impairment using the Telephone Interview for Cognitive Status. Our primary outcome was readiness for discharge, measured using the Readiness for Hospital Discharge Scale. We used multivariable linear regression to test the independent association of each survivorship factor and readiness for discharge. RESULTS: We included 110 patients (64% male, 88% white, mean age 59 [standard deviation ± 13.1 years]). Emotional distress, functional dependence, and social support were independently associated with readiness for discharge (adjusted ß's [absolute value]: 0.25-0.30, all p < 0.05). CONCLUSIONS: Hospital systems should consider implementing routine in-hospital screening for emotional distress, social support, and functional dependence for CA survivors who are awake, alert and approaching hospital discharge, and prioritize brief in hospital treatment or post-discharge referrals.

Brain computed tomography after resuscitation from in-hospital cardiac arrest.

BACKGROUND: Few data characterize the role of brain computed tomography (CT) after resuscitation from in-hospital cardiac arrest (IHCA). We hypothesized that identifying a neurological etiology of arrest or cerebral edema on brain CT are less common after IHCA than after resuscitation from out-of-hospital cardiac arrest (OHCA). METHODS: We included all patients comatose after resuscitation from IHCA or OHCA in this retrospective cohort analysis. We abstracted patient and arrest clinical characteristics, as well as pH and lactate, to estimate systemic illness severity. Brain CT characteristics included quantitative measurement of the grey-to-white ratio (GWR) at the level of the basal ganglia and qualitative assessment of sulcal and cisternal effacement. We compared GWR distribution by stratum (no edema ≥1.30, mild-to-moderate <1.30 and >1.20, severe ≤1.20) and newly identified neurological arrest etiology between IHCA and OHCA groups. RESULTS: We included 2,306 subjects, of whom 420 (18.2%) suffered IHCA. Fewer IHCA subjects underwent post-arrest brain CT versus OHCA subjects (149 (35.5%) vs 1,555 (82.4%), p < 0.001). Cerebral edema for IHCA versus OHCA was more often absent (60.1% vs. 47.5%) or mild-to-moderate (34.3% vs. 27.9%) and less often severe (5.6% vs. 24.6%). A neurological etiology of arrest was identified on brain CT in 0.5% of IHCA versus 3.2% of OHCA. CONCLUSIONS: Although severe edema was less frequent in IHCA relative to OHCA, mild-to-moderate or severe edema occurred in one in three patients after IHCA. Unsuspected neurological etiologies of arrest were rarely discovered by CT scan in IHCA patients.

Using a theory-based, customized video game as an educational tool to improve physicians' trauma triage decisions: study protocol for a randomized cluster trial.

BACKGROUND: Transfer of severely injured patients to trauma centers, either directly from the field or after evaluation at non-trauma centers, reduces preventable morbidity and mortality. Failure to transfer these patients appropriately (i.e., under-triage) remains common, and occurs in part because physicians at non-trauma centers make diagnostic errors when evaluating the severity of patients' injuries. We developed Night Shift, a theory-based adventure video game, to recalibrate physician heuristics (intuitive judgments) in trauma triage and established its efficacy in the laboratory. We plan a type 1 hybrid effectiveness-implementation trial to determine whether the game changes physician triage decisions in real-life and hypothesize that it will reduce the proportion of patients under-triaged. METHODS: We will recruit 800 physicians who work in the emergency departments (EDs) of non-trauma centers in the US and will randomize them to the game (intervention) or to usual education and training (control). We will ask those in the intervention group to play Night Shift for 2 h within 2 weeks of enrollment and again for 20 min at quarterly intervals. Those in the control group will receive only usual education (i.e., nothing supplemental). We will then assess physicians' triage practices for older, severely injured adults in the 1-year following enrollment, using Medicare claims, and will compare under-triage (primary outcome), 30-day mortality and re-admissions, functional independence, and over-triage between the two groups. We will evaluate contextual factors influencing reach, adoption, implementation, and maintenance with interviews of a subset of trial participants (n = 20) and of other key decision makers (e.g., patients, first responders, administrators [n = 100]). DISCUSSION: The results of the trial will inform future efforts to improve the implementation of clinical practice guidelines in trauma triage and will provide deeper understanding of effective strategies to reduce diagnostic errors during time-sensitive decision making. TRIAL REGISTRATION: ClinicalTrials.gov; NCT06063434 . Registered 26 September 2023.

Recent Advances in Group-Based Trajectory Modeling for Clinical Research.

Group-based trajectory modeling (GBTM) identifies groups of individuals following similar trajectories of one or more repeated measures. The categorical nature of GBTM is particularly well suited to clinical psychology and medicine, where patients are often classified into discrete diagnostic categories. This review highlights recent advances in GBTM and key capabilities that remain underappreciated in clinical research. These include accounting for nonrandom subject attrition, joint trajectory and multitrajectory modeling, the addition of the beta distribution to modeling options, associating trajectories with future outcomes, and estimating the probability of future outcomes. Also discussed is an approach to selecting the number of trajectory groups. Expected final online publication date for the Annual Review of Clinical Psychology, Volume 20 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

A multi-center, randomized, double blinded, placebo-controlled trial of amantadine to stimulate awakening in comatose patients resuscitated from cardiac arrest.

Objectives: We hypothesized that the administration of amantadine would increase awakening of comatose patients resuscitated from cardiac arrest. Methods: We performed a prospective, randomized controlled pilot trial, randomizing subjects to amantadine 100mg twice daily or placebo for up to 7 days. The study drug was administered between 72-120 hours after resuscitation and patients with absent N20 cortical responses, early cerebral edema, or ongoing malignant electroencephalography patterns were excluded. Our primary outcome was awakening, defined as following two-step commands, within 28 days of cardiac arrest. Secondary outcomes included length of stay, awakening, time to awakening, and neurologic outcome measured by Cerebral Performance Category (CPC) at hospital discharge. We compared the proportion of subjects awakening and hospital survival using Fisher's exact tests and time to awakening and hospital length of stay using Wilcoxon rank sum tests. Results: After 2 years, we stopped the study due to slow enrollment and lapse of funding. We enrolled 14 subjects (12% of goal enrollment), 7 in the amantadine arm and 7 in the placebo arm. The proportion of patients who awakened within 28 days after cardiac arrest did not differ between amantadine (n=2, 28.57%) and placebo groups (n=3, 42.86%) (p = 1.00). There were no differences in secondary outcomes. Study medication was stopped in three (21%) subjects. Adverse events included a recurrence of seizures (n=2; 14%), both of which occurred in the placebo arm. Conclusion: We could not determine the effect of amantadine on awakening in comatose survivors of cardiac arrest due to small sample size.

Prehospital Electroencephalography to Detect Traumatic Brain Injury during Helicopter Transport: A Pilot Observational Cohort Study.

OBJECTIVE: Early recognition of traumatic brain injury (TBI) is important to facilitate time-sensitive care. Electroencephalography (EEG) can identify TBI, but feasibility of EEG has not been evaluated in prehospital settings. We tested the feasibility of obtaining single-channel EEG during air medical transport after trauma. We measured association between quantitative EEG features, early blood biomarkers, and abnormalities on head computerized tomography (CT). METHODS: We performed a pilot prospective, observational study enrolling consecutive patients transported by critical care air ambulance from the scene of trauma to a Level I trauma center. During transport, prehospital clinicians placed a sensor on the patient's forehead to record EEG. We reviewed EEG waveforms and selected 90 seconds of recording for quantitative analysis. EEG data processing included fast Fourier transform to summarize component frequency power in the delta (0-4 Hz), theta (4-8 Hz), and alpha (8-13 Hz) ranges. We collected blood samples on day 1 and day 3 post-injury and measured plasma levels of two brain injury biomarkers (ubiquitin C-terminal hydrolase L1 [UCH-L1] and glial fibrillary acidic protein [GFAP]). We compared predictors between individuals with and without CT-positive TBI findings. RESULTS: Forty subjects were enrolled, with EEG recordings successfully obtained in 34 (85%). Reasons for failure included uncharged battery (n = 5) and user error (n = 1). Data were lost in three cases. Of 31 subjects with data, interpretable EEG signal was recorded in 26 (84%). Mean age was 48 (SD 16) years, 79% were male, and 50% suffered motor vehicle crashes. Eight subjects (24%) had CT-positive TBI. Subjects with and without CT-positive TBI had similar median delta power, alpha power, and theta power. UCH-L1 and GFAP plasma levels did not differ across groups. Delta power inversely correlated with UCH-L1 day 1 plasma concentration (r = -0.60, p = 0.03). CONCLUSIONS: Prehospital EEG acquisition is feasible during air transport after trauma.

Simple approach to quantify hypoxic-ischemic brain injury severity from computed tomography imaging files after cardiac arrest.

BACKGROUND: Grey-white ratio (GWR) can estimate severity of cytotoxic cerebral edema secondary to hypoxic-ischemic brain injury after cardiac arrest and predict progression to death by neurologic criteria (DNC). Current approaches to calculating GWR are not standardized and have variable interrater reliability. We tested if measures of variance of brain density on early computed tomographic (CT) imaging after cardiac arrest could predict DNC. METHODS: We performed a retrospective cohort study, identifying post-arrest patients treated between 2011 and 2020 at our single center. We extracted demographic data from our registry and Digital Imaging and Communication in Medicine (DICOM) files for each patient's first brain CT. We analyzed slices 15-20 of each DICOM, corresponding to the level of the basal ganglia while accommodating differences in patient anatomy. We extracted pixel arrays and converted the radiodensities to Hounsfield units (HU). To focus on brain tissue densities, we excluded HU > 60 and < 10. We calculated the variance of each patient's HU distribution and the difference between the means of a two-group Gaussian finite mixture model. We compared these novel metrics to existing measures of cerebral edema, then randomly divided our data into 80% training and 20% test sets and used logistic regression to predict DNC. RESULTS: Of 1,133 included subjects, 457 (40%) were female, mean (standard deviation) age was 58 (16) years, and 115 (10%) progressed to DNC. CTs were obtained a median [interquartile range] of 4.2 [2.8-5.7] hours post-arrest. Our novel measures correlated weakly with GWR. HU variance, but not difference between mixture model means, differed significantly between subjects with and without sulcal or cistern effacement. GWR outperformed our novel measures in predicting progression to DNC with an area under the receiver operating characteristic curve (AUC) of 0.82, compared to HU variance (AUC = 0.73) and the difference between mixture model means (AUC = 0.56). CONCLUSION: There are differences in the distribution of HU on post-arrest CT in patients with qualitative measures of cerebral edema. Current methods to quantify cerebral edema outperform simple measures of attenuation variance on early brain CT. Further analyses could investigate if these measures of variance, or other distributional characteristics of brain density, have improved predictive performance on brain CTs obtained later in the clinical course or derived from discrete regions of anatomical interest.

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

BACKGROUND: In patients with disorders of consciousness (DoC), laboratory and molecular biomarkers may help define endotypes, identify therapeutic targets, prognosticate outcomes, and guide patient selection in clinical trials. We performed a systematic review to identify common data elements (CDEs) and key design elements (KDEs) for future coma and DoC research. METHODS: The Curing Coma Campaign Biospecimens and Biomarkers work group, composed of seven invited members, reviewed existing biomarker and biospecimens CDEs and conducted a systematic literature review for laboratory and molecular biomarkers using predetermined search words and standardized methodology. Identified CDEs and KDEs were adjudicated into core, basic, supplemental, or experimental CDEs per National Institutes of Health classification based on level of evidence, reproducibility, and generalizability across different diseases through a consensus process. RESULTS: Among existing National Institutes of Health CDEs, those developed for ischemic stroke, traumatic brain injury, and subarachnoid hemorrhage were most relevant to DoC and included. KDEs were common to all disease states and included biospecimen collection time points, baseline indicator, biological source, anatomical location of collection, collection method, and processing and storage methodology. Additionally, two disease core, nine basic, 24 supplemental, and 59 exploratory biomarker CDEs were identified. Results were summarized and generated into a Laboratory Data and Biospecimens Case Report Form (CRF) and underwent public review. A final CRF version 1.0 is reported here. CONCLUSIONS: Exponential growth in biomarkers development has generated a growing number of potential experimental biomarkers associated with DoC, but few meet the quality, reproducibility, and generalizability criteria to be classified as core and basic biomarker and biospecimen CDEs. Identification and adaptation of KDEs, however, contribute to standardizing methodology to promote harmonization of future biomarker and biospecimens studies in DoC. Development of this CRF serves as a basic building block for future DoC studies.

The impact of COVID-19 pandemic on out-of-hospital cardiac arrest: An individual patient data meta-analysis.

AIM: Prior studies have reported increased out-of-hospital cardiac arrests (OHCA) incidence and lower survival during the COVID-19 pandemic. We evaluated how the COVID-19 pandemic affected OHCA incidence, bystander CPR rate and patients' outcomes, accounting for regional COVID-19 incidence and OHCA characteristics. METHODS: Individual patient data meta-analysis of studies which provided a comparison of OHCA incidence during the first pandemic wave (COVID-period) with a reference period of the previous year(s) (pre-COVID period). We computed COVID-19 incidence per 100,000 inhabitants in each of 97 regions per each week and divided it into its quartiles. RESULTS: We considered a total of 49,882 patients in 10 studies. OHCA incidence increased significantly compared to previous years in regions where weekly COVID-19 incidence was in the fourth quartile (>136/100,000/week), and patients in these regions had a lower odds of bystander CPR (OR 0.49, 95%CI 0.29-0.81, p = 0.005). Overall, the COVID-period was associated with an increase in medical etiology (89.2% vs 87.5%, p < 0.001) and OHCAs at home (74.7% vs 67.4%, p < 0.001), and a decrease in shockable initial rhythm (16.5% vs 20.3%, p < 0.001). The COVID-period was independently associated with pre-hospital death (OR 1.73, 95%CI 1.55-1.93, p < 0.001) and negatively associated with survival to hospital admission (OR 0.68, 95%CI 0.64-0.72, p < 0.001) and survival to discharge (OR 0.50, 95%CI 0.46-0.54, p < 0.001). CONCLUSIONS: During the first COVID-19 pandemic wave, there was higher OHCA incidence and lower bystander CPR rate in regions with a high-burden of COVID-19. COVID-19 was also associated with a change in patient characteristics and lower survival independently of COVID-19 incidence in the region where OHCA occurred.

Metabolic Manipulation and Therapeutic Hypothermia.

Hypothermia has multiple physiological effects, including decreasing metabolic rate and oxygen consumption (VO2). There are few human data about the magnitude of change in VO2 with decreases in core temperature. We aimed to quantify to magnitude of reduction in resting VO2 as we reduced core temperature in lightly sedated healthy individuals. After informed consent and physical screening, we cooled participants by rapidly infusing 20 mL/kg of cold (4°C) saline intravenously and placing surface cooling pads on the torso. We attempted to suppress shivering using a 1 mcg/kg intravenous bolus of dexmedetomidine followed by titrated infusion at 1.0 to 1.5 µg/(kg·h). We measured resting metabolic rate VO2 through indirect calorimetry at baseline (37°C) and at 36°C, 35°C, 34°C, and 33°C. Nine participants had mean age 30 (standard deviation 10) years and 7 (78%) were male. Baseline VO2 was 3.36 mL/(kg·min) (interquartile range 2.98-3.76) mL/(kg·min). VO2 was associated with core temperature and declined with each degree decrease in core temperature, unless shivering occurred. Over the entire range from 37°C to 33°C, median VO2 declined 0.7 mL/(kg·min) (20.8%) in the absence of shivering. The largest average decrease in VO2 per degree Celsius was by 0.46 mL/(kg·min) (13.7%) and occurred between 37°C and 36°C in the absence of shivering. After a participant developed shivering, core body temperature did not decrease further, and VO2 increased. In lightly sedated humans, metabolic rate decreases around 5.2% for each 1°C decrease in core temperature from 37°C to 33°C. Because the largest decrease in metabolic rate occurs between 37°C and 36°C, subclinical shivering or other homeostatic reflexes may be present at lower temperatures.

2023 American Heart Association Focused Update on Adult Advanced Cardiovascular Life Support: An Update to the American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care.

Cardiac arrest is common and deadly, affecting up to 700 000 people in the United States annually. Advanced cardiac life support measures are commonly used to improve outcomes. This "2023 American Heart Association Focused Update on Adult Advanced Cardiovascular Life Support" summarizes the most recent published evidence for and recommendations on the use of medications, temperature management, percutaneous coronary angiography, extracorporeal cardiopulmonary resuscitation, and seizure management in this population. We discuss the lack of data in recent cardiac arrest literature that limits our ability to evaluate diversity, equity, and inclusion in this population. Last, we consider how the cardiac arrest population may make up an important pool of organ donors for those awaiting organ transplantation.

Impact of Preexisting Depression and Anxiety on Hospital Readmission and Long-Term Survival After Cardiac Arrest.

BACKGROUND: While sudden cardiac arrest (CA) survivors are at risk for developing psychiatric disorders, little is known about the impact of preexisting mental health conditions on long-term survival or postacute healthcare utilization. We examined the prevalence of preexisting psychiatric conditions in CA patients who survived hospital discharge, characterized incidence and reason for inpatient psychiatry consultation during these patients' acute hospitalizations, and determined the association of pre-CA depression and anxiety with hospital readmission rates and long-term survival. We hypothesized that prior depression or anxiety would be associated with higher hospital readmission rates and lower long-term survival. METHODS: We conducted a retrospective cohort study including patients resuscitated from in- and out-of-hospital CA who survived both admission and discharge from a single hospital between January 1, 2010, and December 31, 2017. We identified patients from our prospective registry, then performed a structured chart review to abstract past psychiatric history, prescription medications for psychiatric conditions, and identify inpatient psychiatric consultations. We used administrative data to identify readmissions within 1 year and vital status through December 31, 2020. We used multivariable Cox regressions controlling for patient demographics, medical comorbidities, discharge Cerebral Performance Category and disposition, depression, and anxiety history to predict long-term survival and hospital readmission. RESULTS: We included 684 subjects. Past depression or anxiety was noted in 24% (n = 162) and 19% (n = 129) of subjects. A minority of subjects (n = 139, 20%) received a psychiatry consultation during the index hospitalization. Overall, 262 (39%) subjects had at least 1 readmission within 1 year. Past depression was associated with an increased hazard of hospital readmission (hazard ratio 1.50, 95% CI 1.11-2.04), while past anxiety was not associated with readmission. Neither depression nor anxiety were independently associated with long-term survival. CONCLUSIONS: Depression is an independent risk factor for hospital readmission in CA survivors.

Treatment patterns and clinician stress related to care of out-of-hospital cardiac arrest patients with a do not attempt resuscitation order.

Objective: This research investigated treatment patterns for out-of-hospital cardiac arrest patients with Do Not Attempt Resuscitation orders in Japanese emergency departments and the associated clinician stress. Methods: A cross-sectional survey was conducted at 9 hospitals in Okayama, Japan, targeting emergency department nurses and physicians. The questionnaire inquired about the last treated out-of-hospital cardiac arrest patient with a Do Not Attempt Resuscitation. We assessed emotional stress on a 0-10 scale and moral distress on a 1-5 scale among clinicians. Results: Of 208 participants, 107 (51%) had treated an out-of-hospital cardiac arrest patient with a Do Not Attempt Resuscitation order in the past 6 months. Of these, 65 (61%) clinicians used a "slow code" due to perceived futility in resuscitation (42/65 [65%]), unwillingness to terminate resuscitation upon arrival (38/65 [59%]), and absence of family at the time of patient's arrival (35/65 [54%]). Female clinicians had higher emotional stress (5 vs. 3; P = 0.007) and moral distress (3 vs. 2; P = 0.002) than males. Nurses faced more moral distress than physicians (3 vs. 2; P < 0.001). Adjusted logistic regression revealed that having performed a "slow code" (adjusted odds ratio, 5.09 [95% CI, 1.68-17.87]) and having greater ethical concerns about "slow code" (adjusted odds ratio, 0.35 [95% CI, 0.19-0.58]) were associated with high stress levels. Conclusions: The prevalent use of "slow code" for out-of-hospital cardiac arrest patients with Do Not Attempt Resuscitation orders underscores the challenges in managing these patients in clinical practice.

Noninvasive and reliable automated detection of spreading depolarization in severe traumatic brain injury using scalp EEG.

BACKGROUND: Spreading depolarizations (SDs) are a biomarker and a potentially treatable mechanism of worsening brain injury after traumatic brain injury (TBI). Noninvasive detection of SDs could transform critical care for brain injury patients but has remained elusive. Current methods to detect SDs are based on invasive intracranial recordings with limited spatial coverage. In this study, we establish the feasibility of automated SD detection through noninvasive scalp electroencephalography (EEG) for patients with severe TBI. METHODS: Building on our recent WAVEFRONT algorithm, we designed an automated SD detection method. This algorithm, with learnable parameters and improved velocity estimation, extracts and tracks propagating power depressions using low-density EEG. The dataset for testing our algorithm contains 700 total SDs in 12 severe TBI patients who underwent decompressive hemicraniectomy (DHC), labeled using ground-truth intracranial EEG recordings. We utilize simultaneously recorded, continuous, low-density (19 electrodes) scalp EEG signals, to quantify the detection accuracy of WAVEFRONT in terms of true positive rate (TPR), false positive rate (FPR), as well as the accuracy of estimating SD frequency. RESULTS: WAVEFRONT achieves the best average validation accuracy using Delta band EEG: 74% TPR with less than 1.5% FPR. Further, preliminary evidence suggests WAVEFRONT can estimate how frequently SDs may occur. CONCLUSIONS: We establish the feasibility, and quantify the performance, of noninvasive SD detection after severe TBI using an automated algorithm. The algorithm, WAVEFRONT, can also potentially be used for diagnosis, monitoring, and tailoring treatments for worsening brain injury. Extension of these results to patients with intact skulls requires further study.

Physical injury to the brain, for example due to head trauma, may worsen over time, resulting in long-term disability or death. A spreading depolarization is a slowly spreading wave in the brain, which, if detected, can be used to predict worsening brain injuries. Current methods to detect spreading depolarizations require surgeries, which are risky and unlikely to be recommended to patients with mild brain injuries. In this work, we develop an automated monitoring technique for non-surgical, non-invasive detection of spreading depolarizations, called WAVEFRONT. We validated the performance of WAVEFRONT in 12 patients with severe brain injury. Our results demonstrate the feasibility of non-invasive detection of spreading depolarizations. Our approach can potentially help clinicians predict outcomes of brain injury patients, and tailor treatments accordingly.

Comparing strategies for prehospital transport to specialty care after cardiac arrest.

Outcomes are better when patients resuscitated from out-of-hospital cardiac arrest (OHCA) are treated at specialty centers. The best strategy to transport patients from the scene of resuscitation to specialty care is unknown. METHODS: We performed a retrospective cohort study. We identified patients treated at a single specialty center after OHCA from 2010 to 2021 and used OHCA geolocations to develop a catchment area using a convex hull. Within this area, we identified short term acute care hospitals, OHCA receiving centers, adult population by census block group, and helicopter landing zones. We determined population-level times to specialty care via: (1) direct ground transport; (2) transport to the nearest hospital followed by air interfacility transfer; and (3) ground transport to air ambulance. We used an instrumental variable (IV) adjusted probit regression to estimate the causal effect of transport strategy on functionally favorable survival to hospital discharge. RESULTS: Direct transport to specialty care by ground to air ambulance had the shortest population-level times from OHCA to specialty care (median 56 [IQR 47-66] minutes). There were 1,861 patients included in IV regression of whom 395 (21%) had functionally favorable survival. Most (n = 1,221, 66%) were transported to the nearest hospital by ground EMS then to specialty care by air. Patient outcomes did not differ across transport strategies in our IV analysis. DISCUSSION: We did not find strong evidence in favor of a particular strategy for transport to specialty care after OHCA. Population level time to specialty care was shortest with ground ambulance transport to the nearest helicopter landing zone.