Incidence and Outcomes of Acute Myocardial Infarction During Motor Vehicle Accident Related Hospitalizations

2019 ◽  
Vol 123 (5) ◽  
pp. 725-728 ◽  
Author(s):  
Mohamad Alkhouli ◽  
Fahad Alqahtani
CHEST Journal ◽  
2020 ◽  
Vol 158 (4) ◽  
pp. A167-A168
Author(s):  
Manjari Regmi ◽  
Priyanka Parajuli ◽  
Nitin Tandan ◽  
Ruby Maini ◽  
Odalys Lara-Garcia ◽  
...  

2002 ◽  
Vol 13 (7) ◽  
pp. 381-384
Author(s):  
Masatomo Hayashi ◽  
Takeshi Shimada ◽  
Yasunobu Furusawa ◽  
Shigeru Mori ◽  
Kensaku Kagawa ◽  
...  

2012 ◽  
Vol 2012 ◽  
pp. 1-4
Author(s):  
Michael G. Fradley ◽  
Douglas E. Drachman

Although both coronary artery dissection and heparin-induced thrombocytopenia may provoke myocardial infarction, it is extremely rare for both conditions to develop simultaneously in a single patient. We report a case of a 69-year-old woman who sustained a head-on motor vehicle accident with associated chest trauma. During a subsequent hospitalization, she was exposed to subcutaneous heparin and developed significant thrombocytopenia. Shortly thereafter, she re-presented with an acute myocardial infarction. Coronary angiography revealed a spiral dissection with superimposed thrombosis within the right coronary artery, while laboratory testing confirmed the diagnosis of heparin induced thrombocytopenia. She was treated with catheter-based thrombectomy and adjunctive direct thrombin inhibitor therapy, followed by three months of systemic anticoagulation with warfarin. To our knowledge, this represents the first published case of a native vessel myocardial infarction due to the combination of coronary artery dissection and heparin-induced thrombocytopenia.


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 ◽  
...  

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