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Introduction: In the fast-paced environment of Emergency Departments (EDs), biomarkers are essential for the rapid diagnosis and management of critical conditions.
Aim Of The Study: This study evaluates the current clinical practice on key biomarkers in Romanian EDs, addressing the needs of emergency medicine physicians, and the challenges associated with biomarker testing.
Material And Methods: An online survey was sent to physicians working in ED to explore their perceptions, needs, and barriers regarding biomarkers, including Point-of-care (POC). Data was collected anonymously through an online platform and subsequently analyzed.
Results: This survey analyzed data from 168 completed responses, with 95.2% of respondents being specialists in emergency medicine. Procalcitonin and presepsin were most preferred for PoCT, while troponin and D-dimer were highly rated regardless of the testing method, reflecting their utility in sepsis and cardiovascular emergencies. Neuron-specific enolase, interleukin-6, and procalcitonin were the biomarkers considered needed.
Conclusions: The most frequently used biomarkers in ED were troponin, D-dimer, BNP/NT-proBNP, and procalcitonin. NSE, IL-6, and procalcitonin were the most recommended for future integration. High costs, limited availability, and false-positive concerns remain significant challenges in biomarker use.
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http://dx.doi.org/10.2478/jccm-2024-0041 | DOI Listing |
BMC Emerg Med
September 2025
Department of Neurology and Clinical Neuroscience, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
Background: Identifying suspected anterior circulation large-vessel occlusion (aLVO) strokes during emergency calls could enhance dispatch efficiency, particularly in rural areas. However, data on emergency medical dispatchers' (EMDs) ability to recognize aLVO symptoms remain limited. This simulation study aimed to evaluate the feasibility of identifying side-specific arm paresis, side-specific conjugate eye deviation (CED), and aphasia during emergency calls by instructing layperson callers to perform brief, standardized examination steps.
View Article and Find Full Text PDFBMC Psychiatry
September 2025
Zentrum Isartal Am Kloster Schäftlarn, Schäftlarn, Germany.
Background: Patients with mental health conditions represent a significant concern in emergency departments, consistently ranking as the third or fourth most prevalent diagnoses during consultations. Globally, over the past two decades, there was a marked increase in such incidences, largely driven by a rise in nonurgent visits related to somatic complaints. However, the implications of these nonurgent visits for mental health patients remain unclear, and warrant further investigation.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
September 2025
Emergency Department, Helios Spital, Überlingen, Germany.
Background: The increasing amount of data routinely collected on ICUs poses a challenge for clinicians which is aggravated with data-heavy therapies like Continuous Kidney Replacement Therapy (CKRT). We developed the CKRT Supporting Software Prototype (CKRT-SSP), a clinical decision support system for use before, during and after CKRT. The aim of this user experience (UX) study was to prospectively evaluate CKRT-SSP in terms of usability, user experience, and workload in a simulated ICU setting.
View Article and Find Full Text PDFGeroscience
September 2025
Department of Emergency and Internal Medicine, Skåne University Hospital, Malmö, Sweden.
To evaluate a simplified version of the Clinical Frailty Scale (SCFS) among older adults presenting to the emergency department (ED) with acute dyspnea. In this retrospective single-center cohort study, we included patients from the Acute Dyspnea Study (ADYS) cohort. Severity of illness was assessed using the Medical Emergency Triage and Treatment System (METTS).
View Article and Find Full Text PDFImmunol Res
September 2025
Department of Immunology and Allergy, Faculty of Medicine, Necmettin Erbakan University, Konya, Türkiye.
Background: Variants of uncertain significance (VUS) represent a major diagnostic challenge in the interpretation of genetic testing results, particularly in the context of inborn errors of immunity such as severe combined immunodeficiency (SCID). The inconsistency among computational prediction tools often necessitates expensive and time-consuming wet-lab analyses.
Objective: This study aimed to develop disease-specific, multi-class machine learning models using in silico scores to classify SCID-associated genetic variants and improve the interpretation of VUS.