The American journal of emergency medicine
-
Observational Study
Diagnostic accuracy of artificial intelligence for identifying systolic and diastolic cardiac dysfunction in the emergency department.
Cardiac point-of-care ultrasound (POCUS) can evaluate for systolic and diastolic dysfunction to inform care in the Emergency Department (ED). However, accurate assessment can be limited by user experience. Artificial intelligence (AI) has been proposed as a model to increase the accuracy of cardiac POCUS. However, there is limited evidence of the accuracy of AI in the clinical environment. The objective of this study was to determine the diagnostic accuracy of AI for identifying systolic and diastolic dysfunction compared with expert reviewers. ⋯ When compared with expert assessment, AI had high sensitivity and specificity for diagnosing both systolic and diastolic dysfunction.
-
Skateboarding and motorized boards are popular as a recreational activity and mode of transportation. Prior studies have investigated injury patterns from these activities in the pediatric population, but there is little data in the adult population. This study aims to investigate and compare the type and severity of injuries associated with skateboarding and motorized boards. ⋯ Our study shows a high prevalence of upper extremity injuries, regardless of board type. Motorized boards are associated with a higher risk of multiple fractures and hospital admission. Motorized boards likely have increased risk due to their ability to sustain elevated speeds.
-
This study investigated the feasibility of using the Roth score in the emergency setting to make hospitalization or discharge decisions for patients with acute exacerbations of chronic obstructive pulmonary disease (AECOPD). ⋯ The Roth score (only counts) increased in discharged patients after AECOPD treatment. It appears to be a viable method for predicting hospitalization or discharge decisions in patients with AECOPD who present to the emergency department.