Airbag deployment following a motor vehicle accident in pregnancy

1996 ◽  
Vol 88 (4) ◽  
pp. 726-726 ◽  
Author(s):  
C SIMS ◽  
C BOARDMAN ◽  
S FULLER
2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Amy Schumer ◽  
Stephen Contag

Abstract Introduction Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a genetic disorder that can cause fatal tachyarrhythmias brought on by physical or emotional stress. There is little reported in the literature regarding management of CPVT in pregnancy much less during labor. Case presentation A gravida 2, para 1 presented to our high-risk clinic at 15 weeks gestation with known CPVT. The Caucasian female patient had been diagnosed after experiencing a cardiac arrest following a motor vehicle accident and found to have a pathogenic cardiac ryanodine receptor mutation. An implantable cardioverter defibrillator was placed at that time. Her pregnancy was uncomplicated, and she was medically managed with metoprolol, flecainide, and verapamil. Her labor course and successful vaginal delivery were uncomplicated and involved a multidisciplinary team comprising specialists in electrophysiology, maternal fetal medicine, anesthesiology, general obstetrics, lactation, and neonatology. Conclusions CPVT is likely underdiagnosed and, given that cardiovascular disease is a leading cause of death in pregnancy, it is important to bring further awareness to the diagnosis and management of this inherited arrhythmia syndrome in pregnancy.


Cornea ◽  
2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Sotiria Palioura ◽  
Filippos Vingopoulos ◽  
Nikita Likht ◽  
Cristissa M. Piedra ◽  
Elizabeth Fout-Caraza ◽  
...  

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.


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