Emotional facial expression and perioral motor functions of the human auditory cortex.

OBJECTIVE: We investigated the role of transverse temporal gyrus and adjacent cortex (TTG+) in facial expressions and perioral movements. METHODS: In 31 patients undergoing stereo-electroencephalography monitoring, we describe behavioral responses elicited by electrical stimulation within the TTG+. Task-induced high-gamma modulation (HGM), auditory evoked responses, and resting-state connectivity were used to investigate the cortical sites having different types of responses on electrical stimulation. RESULTS: Changes in facial expressions and perioral movements were elicited on electrical stimulation within TTG+ in 9 (29%) and 10 (32%) patients, respectively, in addition to the more common language responses (naming interruptions, auditory hallucinations, paraphasic errors). All functional sites showed auditory task induced HGM and evoked responses validating their location within the auditory cortex, however, motor sites showed lower peak amplitudes and longer peak latencies compared to language sites. Significant first-degree connections for motor sites included precentral, anterior cingulate, parahippocampal, and anterior insular gyri, whereas those for language sites included posterior superior temporal, posterior middle temporal, inferior frontal, supramarginal, and angular gyri. CONCLUSIONS: Multimodal data suggests that TTG+ may participate in auditory-motor integration. SIGNIFICANCE: TTG+ likely participates in facial expressions in response to emotional cues during an auditory discourse.

Early Identification of Candidates for Epilepsy Surgery: A Multicenter, Machine Learning, Prospective Validation Study.

BACKGROUND AND OBJECTIVES: Epilepsy surgery is often delayed. We previously developed machine learning (ML) models to identify candidates for resective epilepsy surgery earlier in the disease course. In this study, we report the prospective validation. METHODS: In this multicenter, prospective, longitudinal cohort study, random forest models were validated at a pediatric epilepsy center consisting of 2 hospitals and 14 outpatient neurology clinic sites and an adult epilepsy center with 2 hospitals and 27 outpatient neurology clinic sites. The models used neurology visit notes, EEG and MRI reports, visit patterns, hospitalizations, and medication, laboratory, and procedure orders to identify candidates for surgery. The models were trained on historical data up to May 10, 2019. Patients with an ICD-10 diagnosis of epilepsy who visited from May 11, 2019, to May 10, 2020, were screened by the algorithm and assigned surgical candidacy scores. The primary outcome was area under the curve (AUC), which was calculated by comparing scores from patients who underwent epilepsy surgery before November 10, 2020, against scores from nonsurgical patients. Nonsurgical patients' charts were reviewed to determine whether patients with high scores were more likely to be missed surgical candidates. Delay to surgery was defined as the time between the first visit that a surgical candidate was identified by the algorithm and the date of the surgery. RESULTS: A total of 5,285 pediatric and 5,782 adult patients were included to train the ML algorithms. During the study period, 41 children and 23 adults underwent resective epilepsy surgery. In the pediatric cohort, AUC was 0.91 (95% CI 0.87-0.94), positive predictive value (PPV) was 0.08 (0.05-0.10), and negative predictive value (NPV) was 1.00 (0.99-1.00). In the adult cohort, AUC was 0.91 (0.86-0.97), PPV was 0.07 (0.04-0.11), and NPV was 1.00 (0.99-1.00). The models first identified patients at a median of 2.1 years (interquartile range [IQR]: 1.2-4.9 years, maximum: 11.1 years) before their surgery and 1.3 years (IQR: 0.3-4.0 years, maximum: 10.1 years) before their presurgical evaluations. DISCUSSION: ML algorithms can identify surgical candidates earlier in the disease course. Even at specialized epilepsy centers, there is room to shorten the time to surgery. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that a machine learning algorithm can accurately distinguish patients with epilepsy who require resective surgery from those who do not.

Acute seizure therapies in people with epilepsy: Fact or fiction? A U.S. Perspective.

Patients with epilepsy (PWE) may experience seizure emergencies including acute repetitive seizures despite chronic treatment with daily antiseizure medications. Seizures may adversely impact routine daily activities and/or healthcare utilization and may impair the quality of life of patients with epilepsy and their caregivers. Seizures often occur at home, school, or work in a community setting. Appropriate treatment that is readily accessible for patients with seizure urgencies and emergencies is essential outside the hospital setting. When determining the best acute antiseizure therapy for PWE, clinicians need to consider all of the available rescue medications and their routes of administration including the safety and efficacy profiles. Benzodiazepines are a standard of care as a rescue therapy, yet there are several misconceptions about their use and safety. Reevaluating potential misconceptions and formulating best practices are necessary to maximize usage for each available option of acute therapy. We examine common beliefs associated with traditional use of acute seizure therapies to refute or support them based on the current level of evidence in the published literature.

Pediatric status epilepticus management by Emergency Medical Services (the pSERG cohort).

PURPOSE: Delayed treatment in status epilepticus (SE) is independently associated with increased treatment resistance, morbidity, and mortality. We describe the prehospital management pathway and Emergency Medical Services (EMS) timeliness in children who developed refractory convulsive status epilepticus (RCSE). METHODS: Retrospective multicenter study in the United States using prospectively collected observational data from June 2011 to March 2020. We selected pediatric patients (one month-21 years) with RCSE initiated outside the hospital and transported to the hospital by EMS. RESULTS: We included 91 patients with a median (percentile25-percentile75) age of 3.0 (1.5-7.3) years. The median time from seizure onset to hospital arrival was 45 (30-67) minutes, with a median time cared for by EMS of 24 (15-36) minutes. Considering treatment by caregivers and EMS before hospital arrival, 20 (22%) patients did not receive any anti-seizure medications (ASM) and 71 (78%) received one to five doses of benzodiazepines (BZD), without non-BZD ASM. We provided the prehospital treatment flow path of these patients through caregivers and EMS including relevant time points. Patients with a history of SE were more likely to receive the first BZD in the prehospital setting compared to patients without a history of SE (adjusted HR 3.25, 95% CI 1.72-6.12, p<0.001). CONCLUSION: In this multicenter study of pediatric RCSE, prehospital treatment may be streamlined further. Patients with a history of SE were more likely to receive prehospital rescue medication.

Neuropsychological outcomes after epilepsy surgery: A comparison of stereo electroencephalography and subdural electrodes.

BACKGROUND AND PURPOSE: We analyzed the association of neuropsychological outcomes after epilepsy surgery with the intracranial electrode type (stereo electroencephalography [SEEG] and subdural electrodes [SDE]), and electrical stimulation mapping (ESM) of speech/language. METHODS: Drug-resistant epilepsy patients who underwent comprehensive neuropsychological evaluation before and 1 year after epilepsy surgery were included. SEEG and SDE subgroups were matched by age, handedness, operated hemisphere, and seizure freedom. Postsurgical neuropsychological outcomes (adjusted for presurgical scores) and reliable change indices were analyzed as functions of electrode type and ESM. RESULTS: Ninety-nine patients aged 6-29 years were included with similar surgical resection/ablation volumes in the SEEG and SDE subgroups. Most of the neuropsychological outcomes were comparable between SEEG and SDE subgroups; however, Working Memory and Processing Speed were significantly improved in the SEEG subgroup. Undergoing language ESM was associated with significant improvements in Spelling, Letter-Word Identification, Vocabulary, Verbal Comprehension, Verbal Learning, and Story Memory scores, but a decline in Calculation scores. CONCLUSIONS: Intracranial evaluations with SEEG and SDE are comparable in terms of long-term postsurgical neuropsychological outcomes. Our data suggest that SEEG may be associated with improvements in working memory and processing speed, representing cognitive domains served by spatially distributed networks. Our study also supports wider use of language ESM before epilepsy surgery, preferably using other language tasks in addition to visual naming. Rather than the type of electrode, postsurgical neuropsychological outcomes are driven by whether language ESM was performed or not, with beneficial effects of language mapping.

Model-Informed Precision Dosing Guidance of Ethosuximide Developed from a Randomized Controlled Clinical Trial of Childhood Absence Epilepsy.

Ethosuximide was identified as the optimal option for new-onset childhood absence epilepsy (CAE) in a randomized, two-phase dose escalation comparative effectiveness trial of ethosuximide, lamotrigine, and valproic acid. However, 47% of ethosuximide initial monotherapy participants experienced short-term treatment failure. This study aimed to characterize the initial monotherapy ethosuximide exposure-response relationship and to propose model-informed precision dosing guidance. Dose titration occurred over a 16-20-week period until patients experienced seizure freedom or intolerable side effects. Subjects with initial monotherapy failure were randomized to one of the other two medications and dose escalation was repeated. A population pharmacokinetic model was created using plasma concentration data (n = 1,320), collected at 4-week intervals from 211 unique participants during both the initial and second monotherapy phases. A logistic regression analysis was performed on the initial monotherapy cohort (n = 103) with complete exposure-response data. Eighty-four participants achieved seizure freedom with a wide range of ethosuximide area under the curves (AUC) ranging from 420 to 2,420 µg·h/mL. AUC exposure estimates for achieving a 50% and 75% probability of seizure freedom were 1,027 and 1,489 µg·h/mL, respectively, whereas the corresponding cumulative frequency of intolerable adverse events was 11% and 16%. Monte Carlo Simulation indicated a daily dose of 40 and 55 mg/kg to achieve 50% and 75% probability of seizure freedom in the overall population, respectively. We identified the need for adjusted mg/kg dosing in different body weight cohorts. This ethosuximide proposed model-informed precision dosing guidance to achieve seizure freedom carries promise to optimize initial monotherapy success for patients with CAE.

Early Clinical Variables Associated With Refractory Convulsive Status Epilepticus in Children.

BACKGROUND AND OBJECTIVES: The objective of this study was to determine patient-specific factors known proximate to the presentation to emergency care associated with the development of refractory convulsive status epilepticus (RSE) in children. METHODS: An observational case-control study was conducted comparing pediatric patients (1 month-21 years) with convulsive SE whose seizures stopped after benzodiazepine (BZD) and a single second-line antiseizure medication (ASM) (responsive established status epilepticus [rESE]) with patients requiring more than a BZD and a single second-line ASM to stop their seizures (RSE). These subpopulations were obtained from the pediatric Status Epilepticus Research Group study cohort. We explored clinical variables that could be acquired early after presentation to emergency medical services with univariate analysis of the raw data. Variables with p < 0.1 were retained for univariable and multivariable regression analyses. Multivariable logistic regression models were fit to age-matched and sex-matched data to obtain variables associated with RSE. RESULTS: We compared data from a total of 595 episodes of pediatric SE. Univariate analysis demonstrated no differences in time to the first BZD (RSE 16 minutes [IQR 5-45]; rESE 18 minutes [IQR 6-44], p = 0.068). Time to second-line ASM was shorter in patients with RSE (RSE 65 minutes; rESE 70 minutes; p = 0.021). Both univariable and multivariable regression analyses revealed a family history of seizures (OR 0.37; 95% CI 0.20-0.70, p = 0.0022) or a prescription for rectal diazepam (OR 0.21; 95% CI 0.078-0.53, p = 0.0012) was associated with decreased odds of RSE. DISCUSSION: Time to initial BZD or second-line ASM was not associated with progression to RSE in our cohort of patients with rESE. A family history of seizures and a prescription for rectal diazepam were associated with a decreased likelihood of progression to RSE. Early attainment of these variables may help care for pediatric rESE in a more patient-tailored manner. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that patient and clinical factors may predict RSE in children with convulsive seizures.

Developmental Epidemiology of Pediatric Anxiety Disorders.

This review summarizes the developmental epidemiology of childhood and adolescent anxiety disorders. It discusses the coronavirus disease of 2019 (COVID-19) pandemic, sex differences, longitudinal course, and stability of anxiety disorders in addition to recurrence and remission. The trajectory of anxiety disorders-whether homotypic (ie, the same anxiety disorder persists over time) or heterotypic (ie, an anxiety disorder shifts to a different diagnosis over time) is discussed with regard to social, generalized, and separation anxiety disorders as well as specific phobia, and panic disorder. Finally, strategies for early recognition, prevention, and treatment of disorders are discussed.

Automated, machine learning-based alerts increase epilepsy surgery referrals: A randomized controlled trial.

OBJECTIVE: To determine whether automated, electronic alerts increased referrals for epilepsy surgery. METHODS: We conducted a prospective, randomized controlled trial of a natural language processing-based clinical decision support system embedded in the electronic health record (EHR) at 14 pediatric neurology outpatient clinic sites. Children with epilepsy and at least two prior neurology visits were screened by the system prior to their scheduled visit. Patients classified as a potential surgical candidate were randomized 2:1 for their provider to receive an alert or standard of care (no alert). The primary outcome was referral for a neurosurgical evaluation. The likelihood of referral was estimated using a Cox proportional hazards regression model. RESULTS: Between April 2017 and April 2019, at total of 4858 children were screened by the system, and 284 (5.8%) were identified as potential surgical candidates. Two hundred four patients received an alert, and 96 patients received standard care. Median follow-up time was 24 months (range: 12-36 months). Compared to the control group, patients whose provider received an alert were more likely to be referred for a presurgical evaluation (3.1% vs 9.8%; adjusted hazard ratio [HR] = 3.21, 95% confidence interval [CI]: 0.95-10.8; one-sided p = .03). Nine patients (4.4%) in the alert group underwent epilepsy surgery, compared to none (0%) in the control group (one-sided p = .03). SIGNIFICANCE: Machine learning-based automated alerts may improve the utilization of referrals for epilepsy surgery evaluations.

Implementation of CYP2D6-guided opioid therapy at Cincinnati Children's Hospital Medical Center.

PURPOSE: We describe the implementation of CYP2D6-focused pharmacogenetic testing to guide opioid prescribing in a quaternary care, nonprofit pediatric academic medical center. SUMMARY: Children are often prescribed oral opioids after surgeries, for cancer pain, and occasionally for chronic pain. In 2004, Cincinnati Children's Hospital Medical Center implemented pharmacogenetic testing for CYP2D6 metabolism phenotype to inform codeine prescribing. The test and reports were updated to align with changes over time in the testing platform, the interpretation of genotype to phenotype, the electronic health record, and Food and Drug Administration (FDA) guidance. The use of the test increased when a research project required testing and decreased as prescribing of oxycodone increased due to FDA warnings about codeine. Education about the opioid-focused pharmacogenetic test was provided to prescribers (eg, the pain and sickle cell teams) as well as patients and families. Education and electronic health record capability increased provider compliance with genotype-guided postsurgical prescribing of oxycodone, although there was a perceived lack of utility for oxycodone prescribing. CONCLUSION: The implementation of pharmacogenetic testing to inform opioid prescribing for children has evolved with accumulating evidence and guidelines, requiring changes in reporting of results and recommendations.

Implementation of Machine Learning Pipelines for Clinical Practice: Development and Validation Study.

BACKGROUND: Artificial intelligence (AI) technologies, such as machine learning and natural language processing, have the potential to provide new insights into complex health data. Although powerful, these algorithms rarely move from experimental studies to direct clinical care implementation. OBJECTIVE: We aimed to describe the key components for successful development and integration of two AI technology-based research pipelines for clinical practice. METHODS: We summarized the approach, results, and key learnings from the implementation of the following two systems implemented at a large, tertiary care children's hospital: (1) epilepsy surgical candidate identification (or epilepsy ID) in an ambulatory neurology clinic; and (2) an automated clinical trial eligibility screener (ACTES) for the real-time identification of patients for research studies in a pediatric emergency department. RESULTS: The epilepsy ID system performed as well as board-certified neurologists in identifying surgical candidates (with a sensitivity of 71% and positive predictive value of 77%). The ACTES system decreased coordinator screening time by 12.9%. The success of each project was largely dependent upon the collaboration between machine learning experts, research and operational information technology professionals, longitudinal support from clinical providers, and institutional leadership. CONCLUSIONS: These projects showcase novel interactions between machine learning recommendations and providers during clinical care. Our deployment provides seamless, real-time integration of AI technology to provide decision support and improve patient care.

Diurnal rhythms of spontaneous intracranial high-frequency oscillations.

OBJECTIVE: Seizures are known to occur with diurnal and other rhythms. To gain insight into the neurophysiology of periodicity of seizures, we tested the hypothesis that intracranial high-frequency oscillations (HFOs) show diurnal rhythms and sleep-wake cycle variation. We further hypothesized that HFOs have different rhythms within and outside the seizure-onset zone (SOZ). METHODS: In drug-resistant epilepsy patients undergoing stereotactic-EEG (SEEG) monitoring to localize SOZ, we analyzed the number of 50-200 Hz HFOs/channel/minute (HFO density) through a 24-hour period. The distribution of HFO density during the 24-hour period as a function of the clock time was analyzed with cosinor model, and for non-uniformity with the sleep-wake cycle. RESULTS: HFO density showed a significant diurnal rhythm overall and both within and outside SOZ. This diurnal rhythm of HFO density showed significantly lower amplitude and longer acrophase within SOZ compared to outside SOZ. The peaks of difference in HFO density within and outside SOZ preceded the seizures by approximately 4 hours. The difference in HFO density within and outside SOZ also showed a non-uniform distribution as a function of sleep-wake cycle, with peaks at first hour after arousal and ±2 hours around sleep onset. CONCLUSIONS: Our study shows that the diurnal rhythm of intracranial HFOs is more robust outside the SOZ. This suggests cortical tissue within SOZ generates HFOs relatively more uniformly throughout the day with attenuation of expected diurnal rhythm. The difference in HFO density within and outside SOZ also showed non-uniform distribution according to clock times and the sleep-wake cycle, which can be a potential biomarker for preferential times of pathological cortical excitability. A temporal correlation with seizure occurrence further substantiates this hypothesis.

A machine-learning approach for predicting impaired consciousness in absence epilepsy.

Behavior during 3-4 Hz spike-wave discharges (SWDs) in absence epilepsy can vary from obvious behavioral arrest to no detectible deficits. Knowing if behavior is impaired is crucial for clinical care but may be difficult to determine without specialized behavioral testing, often inaccessible in practice. We aimed to develop a pure electroencephalography (EEG)-based machine-learning method to predict SWD-related behavioral impairment. Our classification goals were 100% predictive value, with no behaviorally impaired SWDs misclassified as spared; and maximal sensitivity. First, using labeled data with known behavior (130 SWDs in 34 patients), we extracted EEG time, frequency domain, and common spatial pattern features and applied support vector machines and linear discriminant analysis to classify SWDs as spared or impaired. We evaluated 32 classification models, optimized with 10-fold cross-validation. We then generalized these models to unlabeled data (220 SWDs in 41 patients), where behavior during individual SWDs was not known, but observers reported the presence of clinical seizures. For labeled data, the best classifier achieved 100% spared predictive value and 93% sensitivity. The best classifier on the unlabeled data achieved 100% spared predictive value, but with a lower sensitivity of 35%, corresponding to a conservative classification of 8 patients out of 23 as free of clinical seizures. Our findings demonstrate the feasibility of machine learning to predict impaired behavior during SWDs based on EEG features. With additional validation and optimization in a larger data sample, applications may include EEG-based prediction of driving safety, treatment adjustment, and insight into mechanisms of impaired consciousness in absence seizures.

Neuronal Circuits Supporting Development of Visual Naming Revealed by Intracranial Coherence Modulations.

Background: Improvement in visual naming abilities throughout the childhood and adolescence supports development of higher-order linguistic skills. We investigated neuronal circuits underlying improvement in the speed of visual naming with age, and age-related dynamics of these circuits. Methods: Response times were electronically measured during an overt visual naming task in epilepsy patients undergoing stereo-EEG monitoring. Coherence modulations among pairs of neuroanatomic parcels were computed and analyzed for relationship with response time and age. Results: During the overt visual naming task, mean response time (latency) significantly decreased from 4 to 23 years of age. Coherence modulations during visual naming showed that increased connectivity between certain brain regions, particularly that between left fusiform gyrus/left parahippocampal gyrus and left frontal operculum, is associated with improvement in naming speed. Also, decreased connectivity in other brain regions, particularly between left angular and supramarginal gyri, is associated with decreased mean response time. Further, coherence modulations between left frontal operculum and both left fusiform and left posterior cingulate gyri significantly increase, while that between left angular and supramarginal gyri significantly decrease, with age. Conclusion: Naming speed continues to improve from pre-school years into young adulthood. This age-related improvement in efficiency of naming environmental objects occurs likely because of strengthened direct connectivity between semantic and phonological nodes, and elimination of intermediate higher-order cognitive steps.

A distributed network supports spatiotemporal cerebral dynamics of visual naming.

OBJECTIVE: Cerebral spatiotemporal dynamics of visual naming were investigated in epilepsy patients undergoing stereo-electroencephalography (SEEG) monitoring. METHODS: Brain networks were defined by Parcel-Activation-Resection-Symptom matching (PARS) approach by matching high-gamma (50-150 Hz) modulations (HGM) in neuroanatomic parcels during visual naming, with neuropsychological outcomes after resection/ablation of those parcels. Brain parcels with >50% electrode contacts simultaneously showing significant HGM were aligned, to delineate spatiotemporal course of naming-related HGM. RESULTS: In 41 epilepsy patients, neuroanatomic parcels showed sequential yet temporally overlapping HGM course during visual naming. From bilateral occipital lobes, HGM became increasingly left lateralized, coursing through limbic system. Bilateral superior temporal HGM was noted around response time, and right frontal HGM thereafter. Correlations between resected/ablated parcels, and post-surgical neuropsychological outcomes showed specific regional groupings. CONCLUSIONS: Convergence of data from spatiotemporal course of HGM during visual naming, and functional role of specific parcels inferred from neuropsychological deficits after resection/ablation of those parcels, support a model with six cognitive subcomponents of visual naming having overlapping temporal profiles. SIGNIFICANCE: Cerebral substrates supporting visual naming are bilaterally distributed with relative hemispheric contribution dependent on cognitive demands at a specific time. PARS approach can be extended to study other cognitive and functional brain networks.

Recommendations for development of acute seizure action plans (ASAPs) from an expert panel.

PURPOSE OF REVIEW: Disease-related treatment action plans for acute exacerbations providing information that may be helpful for self-management for patients and caregivers are commonly used for chronic conditions such as asthma and diabetes. However, among patients with epilepsy, a review of the literature suggested that the majority did not have an action plan in place for acute seizure treatment. RECENT FINDINGS: Currently, there is a lack of unified guidance on seizure action plans (SAPs) in the literature. In the authors' opinion, available formats have limitations for practical use and may not be easily customizable to individual patients, and they are not often designed to provide simple-to-follow steps for rapid immediate steps to determine and initiate appropriate treatment of seizure emergencies. Our group reviewed current examples of SAPs and provided guidance on the development of acute seizure action plans (ASAPs) designed to facilitate rapid, appropriate acute care in the community and to be as useful as possible for a wide range of care partners, including those with limited experience. SUMMARY: This paper provides agreed upon expert opinion recommendations and considerations for goals, development process, types of content, and format for an ASAP.

Benzodiazepine administration patterns before escalation to second-line medications in pediatric refractory convulsive status epilepticus.

OBJECTIVE: This study was undertaken to evaluate benzodiazepine (BZD) administration patterns before transitioning to non-BZD antiseizure medication (ASM) in pediatric patients with refractory convulsive status epilepticus (rSE). METHODS: This retrospective multicenter study in the United States and Canada used prospectively collected observational data from children admitted with rSE between 2011 and 2020. Outcome variables were the number of BZDs given before the first non-BZD ASM, and the number of BZDs administered after 30 and 45 min from seizure onset and before escalating to non-BZD ASM. RESULTS: We included 293 patients with a median (interquartile range) age of 3.8 (1.3-9.3) years. Thirty-six percent received more than two BZDs before escalating, and the later the treatment initiation was after seizure onset, the less likely patients were to receive multiple BZD doses before transitioning (incidence rate ratio [IRR] = .998, 95% confidence interval [CI] = .997-.999 per minute, p = .01). Patients received BZDs beyond 30 and 45 min in 57.3% and 44.0% of cases, respectively. Patients with out-of-hospital seizure onset were more likely to receive more doses of BZDs beyond 30 min (IRR = 2.43, 95% CI = 1.73-3.46, p < .0001) and beyond 45 min (IRR = 3.75, 95% CI = 2.40-6.03, p < .0001) compared to patients with in-hospital seizure onset. Intermittent SE was a risk factor for more BZDs administered beyond 45 min compared to continuous SE (IRR = 1.44, 95% CI = 1.01-2.06, p = .04). Forty-seven percent of patients (n = 94) with out-of-hospital onset did not receive treatment before hospital arrival. Among patients with out-of-hospital onset who received at least two BZDs before hospital arrival (n = 54), 48.1% received additional BZDs at hospital arrival. SIGNIFICANCE: Failure to escalate from BZDs to non-BZD ASMs occurs mainly in out-of-hospital rSE onset. Delays in the implementation of medical guidelines may be reduced by initiating treatment before hospital arrival and facilitating a transition to non-BZD ASMs after two BZD doses during handoffs between prehospital and in-hospital settings.