Management of Patients With Cardiac Arrest Requiring Interfacility Transport: A Scientific Statement From the American Heart Association.

People who experience out-of-hospital cardiac arrest often require care at a regional center for continued treatment after resuscitation, but many do not initially present to the hospital where they will be admitted. For patients who require interfacility transport after cardiac arrest, the decision to transfer between centers is complex and often based on individual clinical characteristics, resources at the presenting hospital, and available transport resources. Once the decision has been made to transfer a patient after cardiac arrest, there is little direct guidance on how best to provide interfacility transport. Accepting centers depend on transferring emergency departments and emergency medical services professionals to make important and nuanced decisions about postresuscitation care that may determine the efficacy of future treatments. The consequences of early care are greater when transport delays occur, which is common in rural areas or due to inclement weather. Challenges of providing interfacility transfer services for patients who have experienced cardiac arrest include varying expertise of clinicians, differing resources available to them, and nonstandardized communication between transferring and receiving centers. Although many aspects of care are insufficiently studied to determine implications for specific out-of-hospital treatment on outcomes, a general approach of maintaining otherwise recommended postresuscitation care during interfacility transfer is reasonable. This includes close attention to airway, vascular access, ventilator management, sedation, cardiopulmonary monitoring, antiarrhythmic treatments, blood pressure control, temperature control, and metabolic management. Patient stability for transfer, equity and inclusion, and communication also must be considered. Many of these aspects can be delivered by protocol-driven care.

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.

Inside the Black Box of Deliberate Practice: How do Coaches Coach to Improve Trauma Triage.

INTRODUCTION: Deliberate practice, goal-oriented training with feedback from a coach, is a common tool for improving physicians' performance. However, little is known about how coaches foster performance improvement. METHODS: A content analysis of video-recorded training sessions was performed to analyze the coaches' behaviors during a pilot randomized trial of deliberate practice in trauma triage. The intervention consisted of three video-conference sessions during which trial physicians, under the supervision of a coach, played a customized video game designed to review trauma triage principles. A multidisciplinary team specified tasks (e.g., create collaborative learning environment) that coaches should complete, and suggested 19 coaching strategies (e.g., encourage culture of error) to allow execution of these tasks. Two independent raters translated those strategies into a coding framework and applied it deductively to the recorded sessions. The frequencies of the coaching strategies were summarized, and tested for variation across coaches and time. RESULTS: Thirty physicians received the intervention across two 1-mo blocks. Most (28 [93%]) completed three sessions, each covering two (interquartile range 1-2) triage principles. Coaches used coaching strategies 18 (interquartile range 14.5-22) times per triage principle, using some often (2-3 times/principle) and others infrequently (<1 time/principle). The three coaches used similar numbers (20 versus 16 versus 18.5, P = 0.07) and types of strategies. However, use increased over time (16.8 [Block 1] versus 20 [Block 2] P = 0.018). CONCLUSIONS: Coaches used 19 coaching strategies to deliver this deliberate practice intervention, with behavior that evolved over time. Future trials should isolate the most potent strategies and should assess the best method of standardizing coaching.

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.

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.

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.

Time to Awakening and Self-Fulfilling Prophecies After Cardiac Arrest.

OBJECTIVES: Withdrawal of life-sustaining therapies for perceived poor neurologic prognosis (WLST-N) is common after resuscitation from cardiac arrest and may bias outcome estimates from models trained using observational data. We compared several approaches to outcome prediction with the goal of identifying strategies to quantify and reduce this bias. DESIGN: Retrospective observational cohort study. SETTING: Two academic medical centers ("UPMC" and "University of Alabama Birmingham" [UAB]). PATIENTS: Comatose adults resuscitated from cardiac arrest. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: As potential predictors, we considered clinical, laboratory, imaging, and quantitative electroencephalography data available early after hospital arrival. We followed patients until death, discharge, or awakening from coma. We used penalized Cox regression with a least absolute shrinkage and selection operator penalty and five-fold cross-validation to predict time to awakening in UPMC patients and then externally validated the model in UAB patients. This model censored patients after WLST-N, considering subsequent potential for awakening to be unknown. Next, we developed a penalized logistic model predicting awakening, which treated failure to awaken after WLST-N as a true observed outcome, and a separate logistic model predicting WLST-N. We scaled and centered individual patients' Cox and logistic predictions for awakening to allow direct comparison and then explored the difference in predictions across probabilities of WLST-N. Overall, 1,254 patients were included, and 29% awakened. Cox models performed well (mean area under the curve was 0.93 in the UPMC test sets and 0.83 in external validation). Logistic predictions of awakening were systematically more pessimistic than Cox-based predictions for patients at higher risk of WLST-N, suggesting potential for self-fulfilling prophecies to arise when failure to awaken after WLST-N is considered as the ground truth outcome. CONCLUSIONS: Compared with traditional binary outcome prediction, censoring outcomes after WLST-N may reduce potential for bias and self-fulfilling prophecies.

Risk Factors and Incidence of Epilepsy after Severe Traumatic Brain Injury.

We determined the incidence of post-traumatic epilepsy after severe traumatic brain injury. Of 392 patients surviving to discharge, cumulative incidence of post-traumatic epilepsy was 25% at 5 years and 32% at 15 years, an increase compared with historical reports. Among patients with one late seizure (>7 days post-trauma), the risk of seizure recurrence was 62% after 1 year and 82% at 10 years. Competing hazards regression identified age, decompressive hemicraniectomy, and intracranial infection as independent predictors of post-traumatic epilepsy. Patients with severe traumatic brain injury and a single late post-traumatic seizure will likely require long-term antiseizure medicines. ANN NEUROL 2022;92:663-669.

Predicting posttraumatic epilepsy using admission electroencephalography after severe traumatic brain injury.

OBJECTIVE: Posttraumatic epilepsy (PTE) develops in as many as one third of severe traumatic brain injury (TBI) patients, often years after injury. Analysis of early electroencephalographic (EEG) features, by both standardized visual interpretation (viEEG) and quantitative EEG (qEEG) analysis, may aid early identification of patients at high risk for PTE. METHODS: We performed a case-control study using a prospective database of severe TBI patients treated at a single center from 2011 to 2018. We identified patients who survived 2 years postinjury and matched patients with PTE to those without using age and admission Glasgow Coma Scale score. A neuropsychologist recorded outcomes at 1 year using the Expanded Glasgow Outcomes Scale (GOSE). All patients underwent continuous EEG for 3-5 days. A board-certified epileptologist, blinded to outcomes, described viEEG features using standardized descriptions. We extracted 14 qEEG features from an early 5-min epoch, described them using qualitative statistics, then developed two multivariable models to predict long-term risk of PTE (random forest and logistic regression). RESULTS: We identified 27 patients with and 35 without PTE. GOSE scores were similar at 1 year (p = .93). The median time to onset of PTE was 7.2 months posttrauma (interquartile range = 2.2-22.2 months). None of the viEEG features was different between the groups. On qEEG, the PTE cohort had higher spectral power in the delta frequencies, more power variance in the delta and theta frequencies, and higher peak envelope (all p < .01). Using random forest, combining qEEG and clinical features produced an area under the curve of .76. Using logistic regression, increases in the delta:theta power ratio (odds ratio [OR] = 1.3, p < .01) and peak envelope (OR = 1.1, p < .01) predicted risk for PTE. SIGNIFICANCE: In a cohort of severe TBI patients, acute phase EEG features may predict PTE. Predictive models, as applied to this study, may help identify patients at high risk for PTE, assist early clinical management, and guide patient selection for clinical trials.

Benefit-aware early prediction of health outcomes on multivariate EEG time series.

Given a cardiac-arrest patient being monitored in the ICU (intensive care unit) for brain activity, how can we predict their health outcomes as early as possible? Early decision-making is critical in many applications, e.g. monitoring patients may assist in early intervention and improved care. On the other hand, early prediction on EEG data poses several challenges: (i) earliness-accuracy trade-off; observing more data often increases accuracy but sacrifices earliness, (ii) large-scale (for training) and streaming (online decision-making) data processing, and (iii) multi-variate (due to multiple electrodes) and multi-length (due to varying length of stay of patients) time series. Motivated by this real-world application, we present BeneFitter that infuses the incurred savings from an early prediction as well as the cost from misclassification into a unified domain-specific target called benefit. Unifying these two quantities allows us to directly estimate a single target (i.e. benefit), and importantly, (a) is efficient and fast, with training time linear in the number of input sequences, and can operate in real-time for decision-making, (b) can handle multi-variate and variable-length time-series, suitable for patient data, and (c) is effective, providing up to 2× time-savings with equal or better accuracy as compared to competitors.

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.

Association of Prehospital Physician Presence During Pediatric Out-of-Hospital Cardiac Arrest With Neurologic Outcomes.

OBJECTIVES: To examine the association of prehospital physician presence with neurologic outcomes of pediatric patients with out-of-hospital cardiac arrest (OHCA). DESIGN: Retrospective cohort study. SETTING: Data from the Japanese Association for Acute Medicine-OHCA Registry. INTERVENTIONS: None. PATIENTS: Pediatric patients (age 17 yr old or younger) registered in the database between June 2014 and December 2019. MEASUREMENT AND MAIN RESULTS: We used logistic regression models with stabilized inverse probability of treatment weighting (IPTW) to estimate the associated treatment effect of a prehospital physician with 1-month neurologically intact survival. Secondary outcomes included in-hospital return of spontaneous circulation (ROSC) and 1-month survival after OHCA. A total of 1,187 patients (276 in the physician presence group and 911 in the physician absence group) were included (median age 3 yr [interquartile range 0-14 yr]; 723 [61%] male). Comparison of the physician presence group, versus the physician absence, showed 1-month favorable neurologic outcomes of 8.3% (23/276) versus 3.6% (33/911). Physician presence was associated with greater odds of 1-month neurologically intact survival after stabilized IPTW adjustment (adjusted odds ratio [aOR] 1.98, 95% CI 1.08-3.66). We also found an association in the secondary outcome between physician presence, opposed to absence, and in-hospital ROSC (aOR 1.48, 95% CI 1.08-2.04). However, we failed to identify an association with 1-month survival (aOR 1.49, 95% CI 0.97-2.88). CONCLUSIONS: Among pediatric patients with OHCA, prehospital physician presence, compared with absence, was associated almost two-fold greater odds of 1-month favorable neurologic outcomes.

The relationship between mindfulness and enduring somatic threat severity in long-term cardiac arrest survivors.

BACKGROUND: Cardiac arrest (CA) survivors experience continuous exposures to potential traumas though chronic cognitive, physical and emotional sequelae and enduring somatic threats (ESTs) (i.e., recurring somatic traumatic reminders of the event). Sources of ESTs can include the daily sensation of an implantable cardioverter defibrillator (ICD), ICD-delivered shocks, pain from rescue compressions, fatigue, weakness, and changes in physical function. Mindfulness, defined as non-judgmental present-moment awareness, is a teachable skill that might help CA survivors cope with ESTs. Here we describe the severity of ESTs in a sample of long-term CA survivors and explore the cross-sectional relationship between mindfulness and severity of ESTs. METHODS: We analyzed survey data of long-term CA survivors who were members of the Sudden Cardiac Arrest Foundation (collected 10-11/2020). We assessed ESTs using 4 cardiac threat items from the Anxiety Sensitivity Index-revised (items range from 0 "very little" to 4 "very much") which we summed to create a score reflecting total EST burden (range 0-16). We assessed mindfulness using the Cognitive and Affective Mindfulness Scale-Revised. First, we summarized the distribution of EST scores. Second, we used linear regression to describe the relationship between mindfulness and EST severity adjusting for age, gender, time since arrest, COVID-19-related stress, and loss of income due to COVID. RESULTS: We included 145 CA survivors (mean age: 51 years, 52% male, 93.8% white, mean time since arrest: 6 years, 24.1% scored in the upper quarter of EST severity). Greater mindfulness (ß: -30, p = 0.002), older age (ß: -0.30, p = 0.01) and longer time since CA (ß: -0.23, p = 0.005) were associated with lower EST severity. Male sex was also associated with greater EST severity (ß: 0.21, p = 0.009). CONCLUSION: ESTs are common among CA survivors. Mindfulness may be a protective skill that CA survivors use to cope with ESTs. Future psychosocial interventions for the CA population should consider using mindfulness as a core skill to reduce ESTs.

Opioid-Associated Out-of-Hospital Cardiac Arrest: Distinctive Clinical Features and Implications for Health Care and Public Responses: A Scientific Statement From the American Heart Association.

Opioid overdose is the leading cause of death for Americans 25 to 64 years of age, and opioid use disorder affects >2 million Americans. The epidemiology of opioid-associated out-of-hospital cardiac arrest in the United States is changing rapidly, with exponential increases in death resulting from synthetic opioids and linear increases in heroin deaths more than offsetting modest reductions in deaths from prescription opioids. The pathophysiology of polysubstance toxidromes involving opioids, asphyxial death, and prolonged hypoxemia leading to global ischemia (cardiac arrest) differs from that of sudden cardiac arrest. People who use opioids may also develop bacteremia, central nervous system vasculitis and leukoencephalopathy, torsades de pointes, pulmonary vasculopathy, and pulmonary edema. Emergency management of opioid poisoning requires recognition by the lay public or emergency dispatchers, prompt emergency response, and effective ventilation coupled to compressions in the setting of opioid-associated out-of-hospital cardiac arrest. Effective ventilation is challenging to teach, whereas naloxone, an opioid antagonist, can be administered by emergency medical personnel, trained laypeople, and the general public with dispatcher instruction to prevent cardiac arrest. Opioid education and naloxone distributions programs have been developed to teach people who are likely to encounter a person with opioid poisoning how to administer naloxone, deliver high-quality compressions, and perform rescue breathing. Current American Heart Association recommendations call for laypeople and others who cannot reliably establish the presence of a pulse to initiate cardiopulmonary resuscitation in any individual who is unconscious and not breathing normally; if opioid overdose is suspected, naloxone should also be administered. Secondary prevention, including counseling, opioid overdose education with take-home naloxone, and medication for opioid use disorder, is important to prevent recurrent opioid overdose.

Mindfulness is inversely associated with psychological symptoms in long-term cardiac arrest survivors.

Identifying correlates of psychological symptoms in cardiac arrest (CA) survivors is a major research priority. In this longitudinal survey study, we evaluated associations between mindfulness, baseline psychological symptoms, and 1-year psychological symptoms in long-term CA survivors. We collected demographic and CA characteristics at baseline. At both timepoints, we assessed posttraumatic stress symptoms (PTS) through the PTSD Checklist-5 (PCL-5) and depression and anxiety symptoms through the Patient Health Questionnaire-4 (PHQ-4). At follow-up, we assessed mindfulness through the Cognitive and Affective Mindfulness Scale-Revised (CAMS-R). We used adjusted linear regression to predict 1-year PCL-5 and PHQ-4 scores, with particular consideration of the CAMS-R as a cross-sectional correlate of outcome. We included 129 CA survivors (mean age: 52 years, 52% male, 98% white). At 1-year follow-up, in adjusted models, CAMS-R (ß: -0.35, p < 0.001) and baseline PCL-5 scores (ß: 0.56, p < 0.001) were associated with 1-year PCL-5 scores. CAMS-R (ß: -0.34, p < 0.001) and baseline PHQ-4 scores were associated with 1-year PHQ-4 scores (ß: 0.37, p < 0.001). In conclusion, mindfulness was inversely associated with psychological symptoms in long-term CA survivors. Future studies should examine the longitudinal relationship of mindfulness and psychological symptoms after CA.

Precision Care in Cardiac Arrest: ICECAP (PRECICECAP) Study Protocol and Informatics Approach.

BACKGROUND: Most trials in critical care have been neutral, in part because between-patient heterogeneity means not all patients respond identically to the same treatment. The Precision Care in Cardiac Arrest: Influence of Cooling duration on Efficacy in Cardiac Arrest Patients (PRECICECAP) study will apply machine learning to high-resolution, multimodality data collected from patients resuscitated from out-of-hospital cardiac arrest. We aim to discover novel biomarker signatures to predict the optimal duration of therapeutic hypothermia and 90-day functional outcomes. In parallel, we are developing a freely available software platform for standardized curation of intensive care unit-acquired data for machine learning applications. METHODS: The Influence of Cooling duration on Efficacy in Cardiac Arrest Patients (ICECAP) study is a response-adaptive, dose-finding trial testing different durations of therapeutic hypothermia. Twelve ICECAP sites will collect data for PRECICECAP from multiple modalities routinely used after out-of-hospital cardiac arrest, including ICECAP case report forms, detailed medication data, cardiopulmonary and electroencephalographic waveforms, and digital imaging and communications in medicine files (DICOMs). We partnered with Moberg Analytics to develop a freely available software platform to allow high-resolution critical care data to be used efficiently and effectively. We will use an autoencoder neural network to create low-dimensional representations of all raw waveforms and derivative features, censored at rewarming to ensure clinical usability to guide optimal duration of hypothermia. We will also consider simple features that are historically considered to be important. Finally, we will create a supervised deep learning neural network algorithm to directly predict 90-day functional outcome from large sets of novel features. RESULTS: PRECICECAP is currently enrolling and will be completed in late 2025. CONCLUSIONS: Cardiac arrest is a heterogeneous disease that causes substantial morbidity and mortality. PRECICECAP will advance the overarching goal of titrating personalized neurocritical care on the basis of robust measures of individual need and treatment responsiveness. The software platform we develop will be broadly applicable to hospital-based research after acute illness or injury.

Survival Rates After Pediatric Traumatic Out-of-Hospital Cardiac Arrest Suggest an Underappreciated Therapeutic Opportunity.

OBJECTIVES: Children with traumatic arrests represent almost one third of annual pediatric out-of-hospital cardiac arrests (OHCAs). However, traumatic arrests are often excluded from study populations because survival posttraumatic arrest is thought to be negligible. We hypothesized that children treated and transported by emergency medical services (EMS) personnel after traumatic OHCA would have lower survival compared with children treated after medical OHCA. METHODS: We performed a secondary, observational study of children younger than 18 years treated and transported by 78 EMS agencies in southwestern Pennsylvania after OHCA from 2010 to 2014. Etiology was determined as trauma or medical by EMS services. We analyzed patient, cardiac arrest, and resuscitation characteristics and ascertained vital status using the National Death Index. We used multivariable logistic regression to test the association of etiology with mortality after covariate adjustment. RESULTS: Forty eight of 209 children (23%) had traumatic OHCA. Children with trauma were older than those with medical OHCA (13.2 [3.8-15.9] vs 0.5 [0.2-2.4] years, P < 0.001). Prehospital return of spontaneous circulation frequency for trauma versus medical etiology was similar (90% vs 87%, P = 0.84). Patients with trauma had higher mortality (69% vs 45% P = 0.004). CONCLUSIONS: More than 8 of 10 children with EMS treated and transported OHCA achieved return of spontaneous circulation. Despite lower survival rates than medical OHCA patients, almost one third of children with a traumatic etiology survived throughout the study period. Future research programs warrant inclusion of children with traumatic OHCA to improve outcomes.

Comparing demographics of organ donor referrals from the intensive care unit and outside units.

The Spanish organ donation system is a world leader in organ recovery. One of Spain's strategies is the identification of organ donor referrals outside of the intensive care unit (ICU) for intensive care to facilitate organ donation (ICOD). There are limited data comparing the profiles of ICU-based and non-ICU ICOD referrals. This single-center retrospective chart review analyzed organ donor referrals of ICU and non-ICU patients to better understand the demographic and clinical differences between cohorts. The primary outcome was to understand if organ donation conversion rates were similar between ICU and non-ICU referrals. We collected data from 745 organ donor referral candidates, 235 (32%) of whom entered ICOD protocols. Out of this cohort, 144 (61%) became actual organ donors, 37 of whom (26%) were referred from non-ICU units. The ICU had the highest organ donor conversion rate (66% of ICU ICOD patients became actual organ donors) whereas non-ICU referrals had a 51% conversion rate. Non-ICU unit donors contributed to 21% and 26% of all kidney and liver donations, respectively. Though organ referral candidates from non-ICU units contribute to a small proportion of actual donors, their donated organs are important to sustaining organ donation and transplant activity.

EMS responses and non-transports during the COVID-19 pandemic.

INTRODUCTION: The COVID-19 pandemic may affect both use of 9-1-1 systems and prehospital treatment and transport practices. We evaluated EMS responses in an EMS region when it experienced low to moderate burden of COVID-19 disease to assess overall trends, response and management characteristics, and non-transport rates. Our goal is to inform current and future pandemic response in similar regions. METHODS: We performed a retrospective review of prehospital EMS responses from 22 urban, suburban, and rural EMS agencies in Western Pennsylvania. To account for seasonal variation, we compared demographic, response, and management characteristics for the 2-month period of March 15 to May 15, 2020 with the corresponding 2-month periods in 2016-2019. We then tested for an association between study period (pandemic vs historical control) and incidence of non-transport in unadjusted and adjusted regression. Finally, we described the continuous trends in responses and non-transports that occurred during the year before and initial phase of the COVID-19 pandemic from January 1, 2019 to May 31, 2020. RESULTS: Among 103,607 EMS responses in the 2-month comparative periods of March 15 to May 15, 2016-2020, we found a 26.5% [95% CI 26.9%, 27.1%] decrease in responses in 2020 compared to the same months from the four prior years. There was a small increase in respiratory cases (0.6% [95%CI 0.1%, 1.1%]) and greater frequency of abnormal vital signs suggesting a sicker patient cohort. There was a relative increase (46.6%) in non-transports between periods. The pandemic period was independently associated with an increase in non-transport (adjusted OR 1.68; 95%CI 1.59, 1.78). Among 177,194 EMS responses occurring in the year before and during the early period of the pandemic, between January 1, 2019, and May 31, 2020, we identified a 31% decrease in responses and a 48% relative increase in non-transports for April 2020 compared to the previous year's monthly averages. CONCLUSION: Despite a low to moderate burden of infection during the initial period of the COVID-19 pandemic, we found a decline in overall EMS response volumes and an increase in the rate of non-transports independent of patient demographics and other response characteristics.

Time to specialty care and mortality after cardiac arrest.

INTRODUCTION: Out of hospital cardiac arrest (OHCA) patients are often transported to the closest emergency department (ED) or cardiac center for initial stabilization and may be transferred for further care. We investigated the effects of delay to transfer on in hospital mortality at a receiving facility. METHODS: We included OHCA patients transported from the ED by a single critical care transport service to a quaternary care facility between 2010 and 2018. We calculated dwell time as time from arrest to critical care transport team contact. We abstracted demographics, arrest characteristics, and interventions started prior to transport arrival. For the primary analysis, we used logistic regression to determine the association of dwell time and in-hospital mortality. As secondary outcomes we investigated for associations of dwell time and mortality within 24 h of arrival, proximate cause of death among decedents, arterial pH and lactate on arrival, sum of worst SOFA subscales within 24 h of arrival, and rearrest during interfacility transport. RESULTS: We included 572 OHCA patients transported from an outside ED to our facility. Median dwell time was 113 (IQR = 85-159) minutes. Measured in 30 min epochs, increasing dwell time was not associated with in-hospital mortality, 24-h mortality, cause of death and initial pH, but was associated with lower 24-h SOFA score (p = 0.01) and lower initial lactate (p = 0.03). Rearrest during transport was rare (n = 29, 5%). Dwell time was associated with lower probability of rearrest during transport (OR = 0.847, (95% CI 0.68-1.01), p = 0.07). CONCLUSIONS: Dwell time was not associated with in-hospital mortality. Rapid transport may be associated with risk of rearrest. Prospective data are needed to clarify optimal patient stabilization and transport strategies.