scholarly journals Posttraumatic Subacute Effusive-Constrictive Pericarditis After a Motor Vehicle Accident

2020 ◽  
Vol 47 (3) ◽  
pp. 233-235
Author(s):  
Melroy S. D'Souza ◽  
Kaitlin Shinn ◽  
Anup D. Patel

Effusive-constrictive pericarditis is typically caused by tuberculosis or other severe inflammatory conditions that affect the pericardium. We report a case of effusive-constrictive pericarditis consequent to a motor vehicle accident. A 32-year-old man with gastroesophageal reflux disease presented with severe substernal chest pain of a month's duration and dyspnea on exertion for one week. Echocardiograms revealed a moderate pericardial effusion, and the diagnosis was subacute effusive-constrictive pericarditis. After thorough tests revealed nothing definitive, we learned that the patient had been in a motor vehicle accident weeks before symptom onset, which made blunt trauma the most likely cause of pericardial injury and effusion. Medical management resolved the effusion and improved his symptoms. To our knowledge, this is the first report of effusion from posttraumatic constrictive pericarditis associated with a motor vehicle accident. We encourage providers to consider recent trauma as a possible cause of otherwise idiopathic pericarditis.

2016 ◽  
Vol 34 ◽  
pp. e3-e4
Author(s):  
Hesham R. Omar ◽  
Engy Helal ◽  
Enrico M. Camporesi

2003 ◽  
Author(s):  
David Walshe ◽  
Elizabeth Lewis ◽  
Kathleen O'Sullivan ◽  
Brenda K. Wiederhold ◽  
Sun I. Kim

1996 ◽  
Vol 35 (04/05) ◽  
pp. 309-316 ◽  
Author(s):  
M. R. Lehto ◽  
G. S. Sorock

Abstract:Bayesian inferencing as a machine learning technique was evaluated for identifying pre-crash activity and crash type from accident narratives describing 3,686 motor vehicle crashes. It was hypothesized that a Bayesian model could learn from a computer search for 63 keywords related to accident categories. Learning was described in terms of the ability to accurately classify previously unclassifiable narratives not containing the original keywords. When narratives contained keywords, the results obtained using both the Bayesian model and keyword search corresponded closely to expert ratings (P(detection)≥0.9, and P(false positive)≤0.05). For narratives not containing keywords, when the threshold used by the Bayesian model was varied between p>0.5 and p>0.9, the overall probability of detecting a category assigned by the expert varied between 67% and 12%. False positives correspondingly varied between 32% and 3%. These latter results demonstrated that the Bayesian system learned from the results of the keyword searches.


Tracheobronchial foreign bodies are a common problem in clinical practice. We present the case of a patient with three aspirated teeth following a motor vehicle accident.


Author(s):  
Tal Margaliot Kalifa ◽  
Misgav Rottenstreich ◽  
Eyal Mazaki ◽  
Hen Y. Sela ◽  
Schwartz Alon ◽  
...  

2021 ◽  
Author(s):  
Gaia S. Pocobelli ◽  
Mary A. Akosile ◽  
Ryan N. Hansen ◽  
Joanna Eavey ◽  
Robert D. Wellman ◽  
...  

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