scholarly journals Prevalence and clinical correlates of dissociative subtype of posttraumatic stress disorder at an outpatient trauma clinic in South Korea

2019 ◽  
Vol 10 (1) ◽  
pp. 1657372 ◽  
Author(s):  
Daeho Kim ◽  
Dongjoo Kim ◽  
Hyunji Lee ◽  
Yubin Cho ◽  
Ji Young Min ◽  
...  
2018 ◽  
Vol 66 (8) ◽  
pp. 393-402 ◽  
Author(s):  
Ji Young Song ◽  
Kyoung-Sook Jeong ◽  
Kyeong-sook Choi ◽  
Min-gi Kim ◽  
Yeon-Soon Ahn

The extent and severity of the psychological effects following chemical release disasters have not been widely reported. The aim of this study was to examine the prevalence of hydrogen fluoride (HF)–related posttraumatic stress disorder (PTSD) and to identify associated psychological risk factors. On September 2012, an estimated 8 to 12 tons of HF gas, which dissolves in air moisture to form droplets of corrosive hydrofluoric acid, escaped from an industrial complex in Gumi, South Korea. Ten months later, structured questionnaires that included items from the Impacts of Event Scale (revised Korean version) as well as questions about demographic and psychological risk factors related to PTSD were distributed to workers in the affected area. The prevalence rate of PTSD was 5.7%. The odds of PTSD in non-alcohol-dependent workers (odds ratio [OR] = 3.10, 95% confidence interval [CI] = [1.27, 7.60]) was significantly higher than in alcohol-independent workers. The OR for PTSD in workers with anxiety (OR = 7.63, 95% CI = [2.10, 27.71) was significantly higher than the OR workers without anxiety. The odds of PTSD in workers with high perceived stress scale (PSS) scores (OR = 8.72, 95 % CI = [2.29, 33.16]) was significantly higher than for workers with low PSS. Alcohol dependence, psychiatric symptoms at the time of the event, anxiety, and high PSS were associated with HF-related PTSD. Long-term employee assistance programs are needed to assist occupational health nurses and clinicians to reduce PTSD after industrial disasters.


2019 ◽  
Vol 21 (3) ◽  
pp. 305-318
Author(s):  
Sarah B. Hill ◽  
Jonathan D. Wolff ◽  
Cara E. Bigony ◽  
Sherry R. Winternitz ◽  
Kerry J. Ressler ◽  
...  

2009 ◽  
Vol 170 (2-3) ◽  
pp. 278-281 ◽  
Author(s):  
Lara A. Ray ◽  
Christy Capone ◽  
Erin Sheets ◽  
Diane Young ◽  
Iwona Chelminski ◽  
...  

2018 ◽  
Vol 49 (12) ◽  
pp. 2049-2059 ◽  
Author(s):  
Andrew A. Nicholson ◽  
Maria Densmore ◽  
Margaret C. McKinnon ◽  
Richard W.J. Neufeld ◽  
Paul A. Frewen ◽  
...  

AbstractBackgroundThe field of psychiatry would benefit significantly from developing objective biomarkers that could facilitate the early identification of heterogeneous subtypes of illness. Critically, although machine learning pattern recognition methods have been applied recently to predict many psychiatric disorders, these techniques have not been utilized to predict subtypes of posttraumatic stress disorder (PTSD), including the dissociative subtype of PTSD (PTSD + DS).MethodsUsing Multiclass Gaussian Process Classification within PRoNTo, we examined the classification accuracy of: (i) the mean amplitude of low-frequency fluctuations (mALFF; reflecting spontaneous neural activity during rest); and (ii) seed-based amygdala complex functional connectivity within 181 participants [PTSD (n = 81); PTSD + DS (n = 49); and age-matched healthy trauma-unexposed controls (n = 51)]. We also computed mass-univariate analyses in order to observe regional group differences [false-discovery-rate (FDR)-cluster corrected p < 0.05, k = 20].ResultsWe found that extracted features could predict accurately the classification of PTSD, PTSD + DS, and healthy controls, using both resting-state mALFF (91.63% balanced accuracy, p < 0.001) and amygdala complex connectivity maps (85.00% balanced accuracy, p < 0.001). These results were replicated using independent machine learning algorithms/cross-validation procedures. Moreover, areas weighted as being most important for group classification also displayed significant group differences at the univariate level. Here, whereas the PTSD + DS group displayed increased activation within emotion regulation regions, the PTSD group showed increased activation within the amygdala, globus pallidus, and motor/somatosensory regions.ConclusionThe current study has significant implications for advancing machine learning applications within the field of psychiatry, as well as for developing objective biomarkers indicative of diagnostic heterogeneity.


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