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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.

Background Emergency medical services (EMS) response volume has been linked to weather and temporal factors in a regional EMS system. We aimed to identify if models of EMS utilization incorporating these data are generalizable through geographically disparate areas in the United States. Methods We performed a retrospective analysis of EMS dispatch data from four regions: New York City, San Francisco, Cincinnati, and Marin County for years 2016–2019. For each model, we used local weather data summarized from the prior 6 h into hourly bins. Our outcome for each model was EMS dispatches as count data. We fit and optimized a negative binomial regression model for each region, to estimate incidence rate ratios. We compared findings to a prior study performed in Western Pennsylvania. Results We included 5,940,637 EMS dispatches from New York City, 809,405 from San Francisco, 260,412 from Cincinnati, and 77,461 from Marin County. Models demonstrated consistency with the Western Pennsylvania model with respect to temperature, season, wind speed, dew point, and time of day; both in terms of direction and effect size when expressed as incidence rate ratios. Precipitation was associated with increasing dispatches in the New York City, Cincinnati, and Marin County models, but not the San Francisco model. Conclusion With minor differences, regional models demonstrated consistent associations between dispatches and time and weather variables. Findings demonstrate the generalizability of associations between these variables with respect to EMS use. Weather and temporal factors should be considered in predictive modeling to optimize EMS staffing and resource allocation.

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.

Background Many of the clinical risk scores routinely used for chest pain assessment have not been validated in patients at high risk for acute coronary syndrome (ACS). We performed an independent comparison of HEART, TIMI, GRACE, FRISC, and PURSUIT scores for identifying chest pain due to ACS and for predicting 30-day death or re-infarction in patients arriving through Emergency Medical Services (EMS). Methods and results We enrolled consecutive EMS patients evaluated for chest pain at three emergency departments. A reviewer blinded to outcome data retrospectively reviewed patient charts to compute each risk score. The primary outcome was ACS diagnosed during the primary admission, and the secondary outcome was death or re-infarction within 30-days of initial presentation. Our sample included 750 patients (aged 59 ± 17 years, 42% female), of whom 115 (15.3%) had ACS and 33 (4.4%) had 30-day death or re-infarction. The c-statistics of HEART, TIMI, GRACE, FRISC, and PURSUIT for identifying ACS were 0.87, 0.86, 0.73, 0.84, and 0.79, respectively, and for predicting 30-day death or re-infarction were 0.70, 0.73, 0.72, 0.72, and 0.62, respectively. Sensitivity/negative predictive value of HEART ≥ 4 and TIMI ≥ 3 for ACS detection were 0.94/0.98 and 0.87/0.97, respectively. Conclusions In chest pain patients admitted through EMS, HEART and TIMI outperform other scores for identifying chest pain due to ACS. Although both have similar negative predictive value, HEART has better sensitivity and lower rate of false negative results, thus it can be used preferentially over TIMI in the initial triage of this population.

This report describes not only the implementation of a coordinated emergency medical services-hospital-based healthcare team but also investigates the integration of nurse-physician teams at a mass gathering medical care event.A review of resource utilization, patient encounters, and local ED census was performed during this period at a college football stadium.During this 4-year period, 1681 patients presented for medical care during 26 events with a total attendance of 1,544,244 (1.09 patients per thousand attendees [PT]). The majority of patient contacts were for minor complaints (1451, 87.6%), whereas 205 (12.4%) received full evaluations (focused history and physical examination most often with pulse oximetric and electrocardiographic monitoring). A total of 109 patients were transported (4.19 PT), representing 6.48% of all patients. Patient census for the event medical deployment increased from 0.44 PT in 2001 to 1.75 PT in 2004. The number and percent of patients transported also increased between 2001 (0.02 PT, 4.48%) and 2004 (0.12 PT, 6.67%). However, 118 (57.6%) patients who received full evaluations were able to be discharged by a physician, avoiding transport. Chief complaints and management of patients receiving full evaluations were consistent across this period, with altered mental status (52.7%) and chest pain (12.7%) as the most common complaints. Average ED census during this period was found to be significantly higher on event days (176.2) than nonevent days (161.2) (t = 8.04, P.001), although this produced only a minor impact on the emergent care system.This study describes one potential deployment plan for a mass gathering medical event and suggests that the incorporation of physicians into a mass gathering setting may be associated with an absolute increase in patient census and transports, while decreasing the percent of patients transported. The impact on local emergency medical services and ED resources, although not specifically investigated in this study, was likely minimal.

This study's purpose was to identify risk factors for return and admission within 72 hours of discharge from the emergency department (ED). During a 2-year period, 104,584 patients were seen and discharged in the ED, and 493 (0.47%) patients returned within 72 hours requiring admission. Risk factors compared were age, sex, race, insurance status, and initial diagnosis. Initial visits were also characterized by weekday, means of arrival, time of arrival and discharge, and time between visits. Older patients, especially over 65 years, and patients with insurance for the elderly (Medicare) were at higher risk. The highest risk initial diagnosis categories were mental disorder (1.2%), genitourinary system (0.93%), and symptom-based diagnoses (0.76%). Also, a high proportion of patients arrived by ambulance. Patients at increased risk of early admission can be identified and should be the first target for prospective prevention strategies that seek to minimize high-risk early returns to the ED.