Diagnostic Evaluation of Patients Presenting with Dyspnea to the Emergency Department Using Bedside Focused Ultrasonography

Objective: One of the most desired aspects in the emergency department is for a diagnostic tool to provide rapid and accurate results. Conventional imaging tools often deliver delayed results with limited data. Based on this premise, we aimed to evaluate the diagnostic accuracy and efficacy of Bedside Focused Ultrasonography (BFUS). Methods: A prospective diagnostic accuracy study was designed. Out of 92 patients presenting to our clinic with dyspnea, 77 were analyzed. Lung, cardiac, and Inferior Vena Cava (IVC) sonographic evaluations were performed on all patients by an investigator blinded to laboratory and imaging results. The agreement between the diagnoses made using BFUS and those determined by the attending physician (considered the gold standard) was compared along with imaging acquisition times. Results: The Kappa values for acute heart failure and pneumonia were found to be 0.946 and 0.863, respectively. Additionally, BFUS was performed in a significantly shorter time compared to chest radiography and computed tomography (p<0.001). Our findings support similar studies in the literature. Conclusion: BFUS can be rapidly applied with high diagnostic accuracy, particularly for acute heart failure and pneumonia, in patients presenting with dyspnea.

Authors

Institutions

Publication Details

Journal
European Journal of Therapeutics
Published
2025-06-11
DOI
https://doi.org/10.58600/eurjther2618
Citations
2
Primary Topic
Ultrasound in Clinical Applications
Type
article
Field-Weighted Citation Impact
3.09

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Diagnostic Evaluation of Patients Presenting with Dyspnea to the Emergency Department Using Bedside Focused Ultrasonography

Cuma Yıldırım, Mustafa Bozkurt
2 citations
European Journal of Therapeutics
Ultrasound in Clinical Applications
3.09
article

Diagnostic Evaluation of Patients Presenting with Dyspnea to the Emergency Department Using Bedside Focused Ultrasonography

Cuma Yıldırım, Mustafa Bozkurt
article en
2 citations

Abstract

Objective: One of the most desired aspects in the emergency department is for a diagnostic tool to provide rapid and accurate results. Conventional imaging tools often deliver delayed results with limited data. Based on this premise, we aimed to evaluate the diagnostic accuracy and efficacy of Bedside Focused Ultrasonography (BFUS). Methods: A prospective diagnostic accuracy study was designed. Out of 92 patients presenting to our clinic with dyspnea, 77 were analyzed. Lung, cardiac, and Inferior Vena Cava (IVC) sonographic evaluations were performed on all patients by an investigator blinded to laboratory and imaging results. The agreement between the diagnoses made using BFUS and those determined by the attending physician (considered the gold standard) was compared along with imaging acquisition times. Results: The Kappa values for acute heart failure and pneumonia were found to be 0.946 and 0.863, respectively. Additionally, BFUS was performed in a significantly shorter time compared to chest radiography and computed tomography (p<0.001). Our findings support similar studies in the literature. Conclusion: BFUS can be rapidly applied with high diagnostic accuracy, particularly for acute heart failure and pneumonia, in patients presenting with dyspnea.

European Journal of TherapeuticsVol. 31(3)
Gaziantep Children's Hospital (TR), Medical Park Gaziantep Hospital (TR)
Gaziantep Üniversitesi
Openalex Percentile: Top 9%
Ultrasound in Clinical Applications
3.09
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.