Abstract
Background
In the last few years network approach has gained traction as a way of understanding psychopathology. Most network analysis in PTSD were conducted on a limited number of patients and looked at the network of PTSD alone. This presentation will outline the results of a network approach analysis that was conducted on a large cohort of veterans taken from the Department Veteran Affairs (VA) national database and compared the both PTSD and PTSD with Depression networks.
Methods
We analyzed two different networks: Only PTSD symptoms using PCL-4 (N=154,700) and PTSD and Depression symptoms (using PHQ9 for depression; N= 33,800). We used Gaussian Graphical Model that estimates pairwise association parameters between all nodes, and least absolute shrinkage and selection operator (LASSO) in order to control for false positive connections. We compared centrality measures within the networks. Finally, networks were also assessed for stability.
Results
Patients age ranged between 18-94 years, mean age was 41.1 (SD=15.1). PCL-4 scores ranged from 17-85, with mean 57.75 (SD=13.85). Within the PCL-PHQ9 data set, PHQ9 scores ranged between 0-27 with mean score of 14.5 (SD=6.27).
When accounting for depression, we found that the most central symptoms involve sleep and concentration (i.e. dysphoric arousal), in contrast to assessing network of PTSD symptoms alone.
Conclusions
Sleep and concentration require more attention from clinicians, especially when PTSD co-occur with depression. Clinicians should monitor these symptoms and in times, should consider focusing firstly on these before starting trauma focused therapy.
In the last few years network approach has gained traction as a way of understanding psychopathology. Most network analysis in PTSD were conducted on a limited number of patients and looked at the network of PTSD alone. This presentation will outline the results of a network approach analysis that was conducted on a large cohort of veterans taken from the Department Veteran Affairs (VA) national database and compared the both PTSD and PTSD with Depression networks.
Methods
We analyzed two different networks: Only PTSD symptoms using PCL-4 (N=154,700) and PTSD and Depression symptoms (using PHQ9 for depression; N= 33,800). We used Gaussian Graphical Model that estimates pairwise association parameters between all nodes, and least absolute shrinkage and selection operator (LASSO) in order to control for false positive connections. We compared centrality measures within the networks. Finally, networks were also assessed for stability.
Results
Patients age ranged between 18-94 years, mean age was 41.1 (SD=15.1). PCL-4 scores ranged from 17-85, with mean 57.75 (SD=13.85). Within the PCL-PHQ9 data set, PHQ9 scores ranged between 0-27 with mean score of 14.5 (SD=6.27).
When accounting for depression, we found that the most central symptoms involve sleep and concentration (i.e. dysphoric arousal), in contrast to assessing network of PTSD symptoms alone.
Conclusions
Sleep and concentration require more attention from clinicians, especially when PTSD co-occur with depression. Clinicians should monitor these symptoms and in times, should consider focusing firstly on these before starting trauma focused therapy.
| Original language | American English |
|---|---|
| Pages (from-to) | S259-S259 |
| Number of pages | 1 |
| Journal | Biological Psychiatry |
| Volume | 87 |
| Issue number | 9, Supplement |
| DOIs | |
| State | Published - 1 May 2020 |
| Externally published | Yes |
Keywords
- Depression
- Network Analysis
- PTSD