Identifying PTSD symptom typologies: A latent class analysis

• Published in: Psychiatry research 2020-03, Vol.285, p.112779-112779, Article 112779
• Main Authors: Campbell, Sarah.B., Trachik, Benjamin, Goldberg, Simon, Simpson, Tracy.L.
• Format: Article
• Language: English
• Subjects: DSM-5; Latent class analysis; NESARC; PTSD; Typologies
• ISSN: 0165-1781; 1872-7123
• DOI: 10.1016/j.psychres.2020.112779

•Latent Class Analysis of DSM 5 PTSD produced four classes.•Classes include Dysphoric, Threat-Reactivity, Low Symptom, and High Symptom.•Classes are differentially associated with demographics, trauma type, comorbidity.•Results may support precision medicine approaches for PTSD. Posttraumatic stress disorder (PTSD) is characterized by re-experiencing, avoidance, negative alterations in cognition and mood, and arousal symptoms per the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5). While numerous symptom combinations are possible to meet diagnostic criteria, simplification of this heterogeneity of symptom presentations may have clinical utility. In a nationally representative sample of American adults with lifetime DSM-5 PTSD diagnoses from the third wave of the National Epidemiologic Survey on Alcohol and Related Conditions (n = 2,365), we used Latent Class Analysis (LCA) to identify qualitatively distinct PTSD symptom typologies. Subsequently, we used linear and logistic regressions to identify demographic, trauma-related, and psychiatric characteristics associated with membership in each class. In contrast to prior LCAs with DSM-IV-TR diagnostic criteria, fit indices for the present analyses of DSM-5 PTSD revealed a four-class solution to the data: Dysphoric (23.8%), Threat-Reactivity (26.1%), High Symptom (33.7%), and Low Symptom (16.3%). Exploratory analyses revealed distinctions between classes in socioeconomic impairment, trauma exposure, comorbid diagnoses, and demographic characteristics. Although the study is limited by its cross-sectional design (preventing analysis of temporal associations or causal pathways between covariates and latent classes), findings may support efforts to develop personalized medicine approaches to PTSD diagnosis and treatment.