Effect of Paralytic Agents on Post-Intubation Sedation in the Emergency Department.

Purpose: This study aimed to evaluate the frequency at which postintubation sedation is administered following use of long-acting paralytic agents compared to short-acting paralytic agents during rapid sequence intubation performed in the emergency department. Methods: This retrospective, single-center study of intubated patients in the emergency department analyzed the difference in time to administration of additional sedation following use of a short-acting paralytic (succinylcholine) compared to use of a long-acting paralytic (rocuronium or vecuronium). A total of 387 patients were available for analysis. The primary outcome was additional sedation given within 15 minutes following administration of a paralytic agent. The secondary outcome sought to evaluate the incidence of hyperkalemia due to paralytic agents by comparing potassium level before and after paralytic administration. Results: 46.9% of patients who received a short-acting paralytic agent received additional sedation within 15 minutes, compared to 40.9% of patients who received a long-acting paralytic agent. The Chi-square analysis comparing the short and long-acting paralytic groups showed no statistically significant difference (χ² [1, N = 387] = 1.24, P = .266) in the frequency of additional sedation administered. Excluding patients who did not receive any additional sedation, the mean time from paralytic administration to additional sedation in all patients was 20.03 ± 18 minutes. No statistically significant difference was detected between groups regarding changes in potassium level. Conclusion: The use of long-acting paralytic agents was not associated with increased time to administration of sedation compared to shortacting paralytic agents. There is an opportunity to reduce the time to sedation administration for intubated patients receiving both short- and long-acting paralytic agents.

RISE-EM: Resident Instruction in Social Emergency Medicine, a Cohort Study of a Novel Curriculum.

There is recognition in the field of emergency medicine (EM) that social determinants of health (SDoH) are key drivers of patient care outcomes. Leaders in EM are calling for curricula integrating SDoH assessment and intervention, public health, and multidisciplinary approaches to EM care throughout medical school and residency. This intersection of SDoH and the emergency care system is known as social emergency medicine (SEM). Currently, there are few resources available for EM training programs to integrate this content; as a result, few EM trainees receive adequate education in SEM. To address this gap, we developed a four-part training in SEM tailored to EM residency programs and medical schools. This curriculum, known as RISE-EM (Resident Instruction in Social Emergency Medicine), uses video lectures, case examples, and group discussions to engage trainees and develop competency in providing sound care that is grounded in evidence-based principles of SEM. In the current study, we tested RISE-EM by delivering the video lectures to residents and medical students in two training programs. We administered pre- and post-course knowledge tests and a post-course participant attitudes survey to assess the acceptability and potential efficacy of the program for improving SEM knowledge and attitudes among EM learners. We found it to be both feasible and acceptable to introduce SEM content in residency conferences, with preliminary data showing statistically significant improvement in knowledge of the content and self-efficacy to apply it to their clinical practice. In summary, RISE-EM has been highly valued by EM learners and viewed as a strong supplement to their existing training, and it has been shown to successfully improve SEM knowledge and attitudes.

Predictors of ICU admission and patient outcome for traumatic brain injury in a Tanzanian referral hospital: Implications for improving treatment guidelines.

Traumatic brain injuries (TBI) are a critical global health challenge, with disproportionate negative impact in low- and middle-income countries (LMICs). People who suffer severe TBI in LMICs are twice as likely to die than those in high-income countries, and survivors experience substantially poorer outcomes. In the hospital, patients with severe TBI are typically seen in intensive care units (ICU) to receive advanced monitoring and lifesaving treatment. However, the quality and outcomes of ICU care in LMICs are often unclear. We analyzed secondary data from a cohort of 605 adult patients who presented to the Emergency Department (ED) of a Tanzanian hospital with a moderate or severe TBI. We examined patient characteristics and performed two binary logistic regression models to assess predictors of ICU admission and patient outcome. Patients were often young (median age = 32, SD = 15), overwhelmingly male (88.9%), and experienced long delays from time of injury to presentation in the ED (median=12 h, SD = 168). A majority of patients (87.8%) underwent surgery and 55.6% ultimately had a "good recovery" with minimal disability, while 34.0% died. Patients were more likely to be seen in the ICU if they had worse baseline symptoms and were over age 60. TBI surgery conveyed a 37% risk reduction for poor TBI outcome. However, ICU patients had a 3.91 times higher risk of poor TBI outcome as compared to those not seen in the ICU, despite controlling for baseline symptoms. The findings point to the need for targeted interventions among young men, improvements in pre-hospital transportation and care, and continued efforts to increase the quality of surgical and ICU care in this setting. It is unlikely that poorer outcome among ICU patients was indicative of poorer care in the ICU; this finding was more likely due to lack of data on several factors that inform care decisions (e.g., comorbid conditions or injuries). Nevertheless, future efforts should seek to increase the capacity of ICUs in low-resource settings to monitor and treat TBI according to international guidelines, and should improve predictive modeling to identify risk for poor outcome.