Thoracic aorta rupture due to deceleration injury in a motor vehicle accident

Author(s):  
Patrizia Gualniera ◽  
Serena Scurria ◽  
Daniela Sapienza ◽  
Alessio Asmundo
2019 ◽  
Vol 11 (3) ◽  
pp. 248-250
Author(s):  
Rupesh Kumar ◽  
Javid Raja ◽  
Ganesh Kumar Munirathinam ◽  
Anand Kumar Mishra ◽  
Rana Sandeep Singh ◽  
...  

Traumatic aortic transection is a life threatening emergency where there is a near-complete tear through all the layers of the aorta due to trauma. This condition is most often lethal and requires immediate medical attention. Symptoms of an aortic rupture may include severe chest pain, back pain, abdominal pain and signs of external chest injury. Treatment should be prompt in hemodynamically unstable patient in the form of endovascular or open surgical technique. We present a twenty nine year old male with aortic transection following motor vehicle accident where an interposition tube graft was placed after trimming the lacerated segments of the aorta under cardiopulmonary bypass. The patient is doing well with two years of follow up at our institution.


1995 ◽  
Vol 10 (3) ◽  
pp. 198-201 ◽  
Author(s):  
Andrew E. Sama ◽  
Douglas P. Barnaby ◽  
Kevin J. Wallis ◽  
Dominick Gadaleta ◽  
Michael H. Hall ◽  
...  

AbstractThe restrained (air bag and seatbelt) driver of a vehicle involved in a high-speed motor-vehicle accident sustained a tear of the thoracic aorta with no signs of external injury. Air bag deployment may mask significant internal injury, and a high index of suspicion is warranted in such situations.


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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