The American journal of emergency medicine
-
Review
Impact of COVID-19 on emergency department management of stroke and STEMI. A narrative review.
The novel coronavirus of 2019 (COVID-19) has resulted in a global pandemic; COVID-19 has resulted in significant challenges in the delivery of healthcare, including emergency management of multiple diagnoses, such as stroke and ST-segment myocardial infarction (STEMI). The aim of this study was to identify the impacts of the COVID-19 pandemic on emergency department care of stroke and STEMI patients. ⋯ Our analysis, using a narrative review format, indicates that there was not a significant change in time required for key interventions for stroke and STEMI emergent management, including imaging (door-to-CT), tPA administration (door-to-needle), angiographic reperfusion (door-to-puncture), and percutaneous coronary intervention (door-to-balloon). Potential future areas of investigation include how emergency department (ED) stroke and STEMI care has adapted in response to different COVID-19 variants and stages of the pandemic, as well as identifying strategies used by EDs that were successful in providing effective emergency care in the face of the pandemic.
-
Multicenter Study
Optimal temperature in targeted temperature management without automated devices using a feedback system: A multicenter study.
Targeted temperature management (TTM) at 32 °C-36 °C improves patient outcomes following out-of-hospital cardiac arrest (OHCA). TTM using automated temperature management devices with feedback systems (TFDs) is recommended, but the equipment is often unavailable. This study aimed to investigate therapeutic relations between targeted temperatures and TFDs on the outcomes of OHCA patients with TTM. ⋯ In OHCA patients receiving TTM without TFDs, the adjusted predicted probability of survival and good neurological outcomes at hospital discharge was greater for TTM at 36 °C than that at 33 °C. This suggests that a TTM of 36 °C rather than 33 °C is associated with more favorable clinical outcomes if TFDs are unavailable.
-
An artificial intelligence (AI) algorithm has been developed to detect the electrocardiographic signature of atrial fibrillation (AF) present on an electrocardiogram (ECG) obtained during normal sinus rhythm. We evaluated the ability of this algorithm to predict incident AF in an emergency department (ED) cohort of patients presenting with palpitations without concurrent AF. ⋯ We found this AI-ECG AF algorithm to maintain statistical significance in predicting incident AF, with clinical utility for screening purposes limited in this ED population with a low incidence of AF.
-
Comment Letter Randomized Controlled Trial
Appraisal of intravenous magnesium sulfate vs. morphine sulfate in relieving renal colic: A randomized clinical trial.