OBJECTIVES/GOALS: Developing pharmacokinetic (PK) models to guide selective serotonin reuptake inhibitor (SSRI) dosing in youth is costly, time-intensive, and requires large numbers of participants. We evaluated the use of remnant blood samples from SSRI-treated youth and developed precision PK dosing strategies. METHODS/STUDY POPULATION: Following IRB approval, we used a clinical surveillance platform to identify patients with routine phlebotomy within 24 hours of escitalopram or sertraline dosing. Remnant blood samples were obtained from youth aged 5–18 years, escitalopram and sertraline concentrations were determined, and clinical characteristics (e.g., age, sex, weight, concomitant medications that inhibit sertraline or escitalopram metabolism) and phenotypes for CYP2C19, the predominant enzyme that metabolizes these SSRIs, were extracted from the electronic medical record (EMR). A population PK analysis of escitalopram and sertraline was performed using NONMEM. The influence of clinical variables, CYP2C19, and dosing was evaluated from simulated concentration-time curves. RESULTS/ANTICIPATED RESULTS: Over 21 months, we collected315 samples from escitalopram-treated patients (N=288) and 265 samples from sertraline-treated patients (N=255). In youth, escitalopram and sertraline exposure (concentrations over time) and specific pharmacokinetic parameters (e.g., clearance) were influenced by CYP2C19 phenotype, concomitant CYP2C19 inhibitors, and patient-specific characteristics. Escitalopram and sertraline concentrations from remnant blood samples were 3.98-fold higher and 3.23-fold higher, respectively, in poor metabolizers compared to normal metabolizers (escitalopram, p<0.001) and compared to normal, rapid, and ultrarapid metabolizers combined (sertraline, p<0.001). DISCUSSION/SIGNIFICANCE: Combining remnant blood sampling with pharmacogenetic-integrated EMR data can facilitate large-scale population PK analyses of escitalopram and sertraline in youth. This real-world approach can be used to rapidly develop precision SSRI dosing strategies, including slower titration and reduced target doses in CYP2C19 poor metabolizers.

The recently published Model Core Content of Disaster Medicine introduces proposed curriculum elements for specialized education and training in Disaster Medicine. This editorial comments on the publishing decision for the manuscript.

We argue that the dual-system approach and, particularly, the default-interventionist framework favored by De Neys unnecessarily constrains process models, limiting their range of application. In turn, the accommodations De Neys makes for these constraints raise questions of parsimony and falsifiability. We conclude that the extent to which processes possess features of system 1 versus system 2 must be tested empirically.

The 2022 update of the Canadian Stroke Best Practice Recommendations (CSBPR) for Acute Stroke Management, 7th edition, is a comprehensive summary of current evidence-based recommendations, appropriate for use by an interdisciplinary team of healthcare providers and system planners caring for persons with an acute stroke or transient ischemic attack. These recommendations are a timely opportunity to reassess current processes to ensure efficient access to acute stroke diagnostics, treatments, and management strategies, proven to reduce mortality and morbidity. The topics covered include prehospital care, emergency department care, intravenous thrombolysis and endovascular thrombectomy (EVT), prevention and management of inhospital complications, vascular risk factor reduction, early rehabilitation, and end-of-life care. These recommendations pertain primarily to an acute ischemic vascular event. Notable changes in the 7th edition include recommendations pertaining the use of tenecteplase, thrombolysis as a bridging therapy prior to mechanical thrombectomy, dual antiplatelet therapy for stroke prevention,1 the management of symptomatic intracerebral hemorrhage following thrombolysis, acute stroke imaging, care of patients undergoing EVT, medical assistance in dying, and virtual stroke care. An explicit effort was made to address sex and gender differences wherever possible. The theme of the 7th edition of the CSBPR is building connections to optimize individual outcomes, recognizing that many people who present with acute stroke often also have multiple comorbid conditions, are medically more complex, and require a coordinated interdisciplinary approach for optimal recovery. Additional materials to support timely implementation and quality monitoring of these recommendations are available at www.strokebestpractices.ca.

OBJECTIVES/GOALS: Identification of COVID-19 patients at risk for deterioration following discharge from the emergency department (ED) remains a clinical challenge. Our objective was to develop a prediction model that identifies COVID-19 patients at risk for return and hospital admission within 30 days of ED discharge. METHODS/STUDY POPULATION: We performed a retrospective cohort study of discharged adult ED patients (n = 7,529) with SARS-CoV-2 infection from 116 unique hospitals contributing to the national REgistry of suspected COVID-19 in EmeRgency care (RECOVER). The primary outcome was return hospital admission within 30 days. Models were developed using Classification and Regression Tree (CART), Gradient Boosted Machine (GBM), Random Forest (RF), and least absolute shrinkage and selection (LASSO) approaches. RESULTS/ANTICIPATED RESULTS: Among COVID-19 patients discharged from the ED on their index encounter, 571 (7.6%) returned for hospital admission within 30 days. The machine learning (ML) models (GBM, RF,: and LASSO) performed similarly. The RF model yielded a test AUC of 0.74 (95% confidence interval [CI] 0.71–0.78) with a sensitivity of 0.46 (0.39-0.54) and specificity of 0.84 (0.82-0.85). Predictive variables including: lowest oxygen saturation, temperature; or history of hypertension,: diabetes, hyperlipidemia, or obesity, were common to all ML models. DISCUSSION/SIGNIFICANCE: A predictive model identifying adult ED patients with COVID-19 at risk for return hospital admission within 30 days is feasible. Ensemble/boot-strapped classification methods outperform the single tree CART method. Future efforts may focus on the application of ML models in the hospital setting to optimize allocation of follow up resources.

Monoclonal antibody therapeutics to treat coronavirus disease (COVID-19) have been authorized by the US Food and Drug Administration under Emergency Use Authorization (EUA). Many barriers exist when deploying a novel therapeutic during an ongoing pandemic, and it is critical to assess the needs of incorporating monoclonal antibody infusions into pandemic response activities. We examined the monoclonal antibody infusion site process during the COVID-19 pandemic and conducted a descriptive analysis using data from 3 sites at medical centers in the United States supported by the National Disaster Medical System. Monoclonal antibody implementation success factors included engagement with local medical providers, therapy batch preparation, placing the infusion center in proximity to emergency services, and creating procedures resilient to EUA changes. Infusion process challenges included confirming patient severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positivity, strained staff, scheduling, and pharmacy coordination. Infusion sites are effective when integrated into pre-existing pandemic response ecosystems and can be implemented with limited staff and physical resources.

Evolution of a solitary wave travelling along a submerged sill is studied. The disturbance from the sill creates a phase lag along the wave crest between the ambient water depth and the shallower depth over the sill. This phase lag causes wave diffraction between the different parts of the wave, which induces radiating waves off the edge of the sill. The radiating waves act as an outlet for wave energy, resulting in significant and continual amplitude reduction of the solitary wave. Findings from laboratory experiments are confirmed numerically by simulating a much longer propagation distance with different sill breadths. When the sill breadth is narrow, the solitary wave slowly attenuates by wave radiation, maintaining a quasi-steady wave pattern. This is not the case for a broader sill. The resulting phase lag on the sill continually changes the wave pattern and the attenuation rate is substantially greater than the rate for the case of the narrow sill. The significant energy radiation together with the continual change in the wave formation eventually leads to the complete annihilation of the solitary wave in a wave tank. We also report a wave-breaking process along the sill observed in laboratory experiments. This breaking is induced when the wave amplitude on the sill is smaller than the maximum amplitude of a solitary wave in a uniform depth. Also found is the wake-like formation of gravity–capillary waves behind the breaking crest forming on the sill. Other features associated with the breaking are presented.

Lithium-ion capacitors (LICs) and Hybrid LICs (H-LICs) were assembled as three-layered pouch cells in an asymmetric configuration employing Faradaic pre-lithiated hard carbon anodes and non-Faradaic ion adsorption-desorption activated carbon (AC) cathodes for LICs and lithium iron phosphate (LiFePO4-LFP)/AC composite cathodes for H-LICs. The room temperature rate performance was evaluated after the initial LIC and H-LIC cell formation as a function of the electrolyte additives. The capacity retention was measured after charging at high temperature conditions, while the design factor explored was electrolyte additive formulation, with a focus on their stability. The high temperature potential holds simulate electrochemical energy materials under extreme environments and act to accelerate the failure mechanisms associated with cell degradation to determine robust electrolyte/additive combinations.

A symptom of mild cognitive impairment (MCI) and Alzheimer’s disease(AD) is a flat learning profile. Learning slope calculation methods vary, andthe optimal method for capturing neuroanatomical changes associated with MCI andearly AD pathology is unclear. This study cross-sectionally compared fourdifferent learning slope measures from the Rey Auditory Verbal Learning Test(simple slope, regression-based slope, two-slope method, peak slope) tostructural neuroimaging markers of early AD neurodegeneration (hippocampalvolume, cortical thickness in parahippocampal gyrus, precuneus, and lateralprefrontal cortex) across the cognitive aging spectrum [normalcontrol (NC); (n=198;age=76±5), MCI (n=370;age=75±7), and AD (n=171;age=76±7)] in ADNI. Within diagnostic group,general linear models related slope methods individually to neuroimagingvariables, adjusting for age, sex, education, and APOE4 status. Among MCI,better learning performance on simple slope, regression-based slope, and lateslope (Trial 2–5) from the two-slope method related to largerparahippocampal thickness (all p-values<.01) andhippocampal volume (p<.01). Better regression-basedslope (p<.01) and late slope(p<.01) were related to larger ventrolateralprefrontal cortex in MCI. No significant associations emerged between any slopeand neuroimaging variables for NC (p-values ≥.05) orAD (p-values ≥.02). Better learning performancesrelated to larger medial temporal lobe (i.e., hippocampal volume,parahippocampal gyrus thickness) and ventrolateral prefrontal cortex in MCIonly. Regression-based and late slope were most highly correlated withneuroimaging markers and explained more variance above and beyond other commonmemory indices, such as total learning. Simple slope may offer an acceptablealternative given its ease of calculation. (JINS, 2015,21, 455–467)