Introduction: Deep vein thrombosis (DVT) is a common cause of admission to the emergency departments (ED). Doppler ultrasonography of the entire lower limb is the first-line imaging modality. But most EDs do not access to full-time radiologists which can lead to delayed diagnosis.
Aim: The aim of this study was to evaluate the diagnostic accuracy of three-point compression ultrasonography performed by emergency medicine resident for diagnosis of DVT.
Methods: This prospective diagnostic study was carried out at Imam Khomeini Hospital in Sari from March 2018 to November 2018. For all patients with suspected lower extremity DVT, first bedside 3-point compression ultrasound were performed by a third year emergency medicine resident at ED. Then Doppler ultrasonography were performed by a radiologist in the radiology department, as a reference test. Sensitivity, specificity, and positive predictive value of the three-point compression ultrasound performed by emergency medicine resident was calculated.
Results: Of the 72 patients enrolled in our study, 50% of the patients were male, with an average age of 36±19 years. The mean of patient admission time to perform ultrasonography by an emergency medicine resident and radiologist were 14.05±19 and 216±140.1 minutes, respectively. The two groups had a statistically significant difference (P<0.0001). In ultrasonography performed by emergency medicine resident and doper ultrasonography by radiologist, 91.67% and 36.1% of patients were diagnosed with DVT, respectively. Although the ultrasonography performed by emergency medicine resident has a relatively low sensitivity (53.8%), it has a good specificity (85.7%). The positive and negative predictive value was 70 and 75%, respectively.
Conclusion: Although the results of this study indicate insufficient sensitivity of bedside three-point compression ultrasound performed by emergency medicine resident in diagnosis of lower limb DVT, the specificity, positive and negative predictive values and positive likelihood ratio were almost appropriate.
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http://dx.doi.org/10.5455/aim.2019.27.119-122 | DOI Listing |
Am J Emerg Med
January 2025
Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT, USA; Center for Outcomes Research and Evaluation, Yale University, New Haven, CT, USA.
Background: This study aimed to examine how physician performance metrics are affected by the speed of other attendings (co-attendings) concurrently staffing the ED.
Methods: A retrospective study was conducted using patient data from two EDs between January-2018 and February-2020. Machine learning was used to predict patient length of stay (LOS) conditional on being assigned a physician of average speed, using patient- and departmental-level variables.
Am J Emerg Med
January 2025
Faculty of Medicine, Universidad de Valladolid, Valladolid, Spain; Emergency Department, Hospital Clínico Universitario, Gerencia Regional de Salud de Castilla y León, Valladolid, Spain.
Background: The study of the inclusion of new variables in already existing early warning scores is a growing field. The aim of this work was to determine how capnometry measurements, in the form of end-tidal CO2 (ETCO2) and the perfusion index (PI), could improve the National Early Warning Score (NEWS2).
Methods: A secondary, prospective, multicenter, cohort study was undertaken in adult patients with unselected acute diseases who needed continuous monitoring in the emergency department (ED), involving two tertiary hospitals in Spain from October 1, 2022, to June 30, 2023.
J Med Internet Res
January 2025
Institute of Medical Teaching and Medical Education Research, University Hospital Würzburg, Würzburg, Germany.
Background: Objective structured clinical examinations (OSCEs) are a widely recognized and accepted method to assess clinical competencies but are often resource-intensive.
Objective: This study aimed to evaluate the feasibility and effectiveness of a virtual reality (VR)-based station (VRS) compared with a traditional physical station (PHS) in an already established curricular OSCE.
Methods: Fifth-year medical students participated in an OSCE consisting of 10 stations.
JMIR AI
January 2025
Department of Radiology, Children's National Hospital, Washington, DC, United States.
Clin Infect Dis
January 2025
Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Infectious Diseases, Respiratory Medicine and Critical Care, Berlin, Germany.
Background: Existing risk evaluation tools underperform in predicting intensive care unit (ICU) admission for patients with the Coronavirus Disease 2019 (COVID-19). This study aimed to develop and evaluate an accurate and calculator-free clinical tool for predicting ICU admission at emergency room (ER) presentation.
Methods: Data from patients with COVID-19 in a nationwide German cohort (March 2020-January 2023) were analyzed.
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