Literature DB >> 20045607

Ability of physicians to diagnose congestive heart failure based on chest X-ray.

Sarah Kennedy1, Barry Simon, Harrison J Alter, Paul Cheung.   

Abstract

BACKGROUND: Chest X-ray interpretation is an important skill in the diagnosis of congestive heart failure (CHF) by emergency physicians.
OBJECTIVES: This study evaluated the ability of emergency physicians to recognize CHF on chest X-ray and the effect of level of training and confidence upon accuracy of interpretation.
METHODS: This was a prospective, blinded study in which 24 patients with an elevated brain natriuretic peptide, low ejection fraction, and diagnosis of CHF were retrospectively identified. In addition, 31 patients without CHF were identified and used as controls. These 55 chest X-rays were presented to emergency attending and housestaff and a radiologist. We calculated the accuracy of the raters' diagnoses, and measured their confidence in that diagnosis and their level of training.
RESULTS: Physicians correctly identified the CHF chest X-rays 79% of the time (sensitivity 59%, specificity 96%; positive likelihood ratio 14.6, negative likelihood ratio 0.43). Accuracy ranged from a low of 78% among first-year residents to a high of 85% among attending, and from 73% (confidence rating of 3/5) to 91% (confidence rating of 5/5). Increasing confidence was significantly correlated with accuracy across the spectrum (p = 0.001). An accuracy of 95% among radiologists suggests that a negative X-ray does not rule out CHF.
CONCLUSIONS: High specificity (96%) and low sensitivity (59%) suggest that emergency physicians are excellent at identifying CHF on X-ray when present, but under-call it frequently. Sensitivity may be much higher in real life given clinical correlation. Both increased level of training and higher confidence significantly improved accuracy. Copyright Â
© 2011 Elsevier Inc. All rights reserved.

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Year:  2010        PMID: 20045607     DOI: 10.1016/j.jemermed.2009.10.018

Source DB:  PubMed          Journal:  J Emerg Med        ISSN: 0736-4679            Impact factor:   1.484


  2 in total

1.  Automated Assessment and Tracking of COVID-19 Pulmonary Disease Severity on Chest Radiographs using Convolutional Siamese Neural Networks.

Authors:  Matthew D Li; Nishanth Thumbavanam Arun; Mishka Gidwani; Ken Chang; Francis Deng; Brent P Little; Dexter P Mendoza; Min Lang; Susanna I Lee; Aileen O'Shea; Anushri Parakh; Praveer Singh; Jayashree Kalpathy-Cramer
Journal:  Radiol Artif Intell       Date:  2020-07-22

Review 2.  Diagnosis of Acute Heart Failure in the Emergency Department: An Evidence-Based Review.

Authors:  Brit Long; Alex Koyfman; Michael Gottlieb
Journal:  West J Emerg Med       Date:  2019-10-24
  2 in total

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