Acute Psychological Stress in Trauma Survivors is Predictive of Post-Traumatic Stress Disorder
Post by Shireen Parimoo
What's the science?
Post-traumatic stress disorder (PTSD) is a psychiatric disorder resulting from a traumatic experience. Symptoms like persistent distress, intrusive thoughts, and flashbacks to the event, among others, can significantly disrupt many aspects of life, such as finding a stable job. People who experience a life-threatening traumatic event are often at risk of developing PTSD. Research shows that self-reported stress and blood biomarkers (e.g., cortisol levels) immediately after trauma are predictive of whether someone is at risk of developing PTSD. However, most hospitals do not screen for PTS symptoms, making it difficult to determine the extent to which psychological stress and physiological factors contribute to the risk of developing PTSD. This week in Nature Medicine, Schultebraucks and colleagues developed and validated an algorithm using medical records and self-reported stress of trauma survivors to predict the trajectory of their PTS symptoms.
How did they do it?
Patients who had experienced a life-threatening traumatic event and were admitted to the emergency department at one of two US hospitals (Grady Memorial, Bellevue Hospital) participated in a prospective longitudinal study. Immediately following the trauma, physiological information like vitals and blood biomarkers were recorded as part of their medical record at the hospitals, along with self-reported measures like immediate stress. These measures were used as predictors to assess the risk of developing PTSD. Following discharge from the hospital, participants’ symptoms were assessed at 1, 3, 6, and 12 months post-trauma using the modified PTSD symptom scale (mPSS; Grady Memorial) or the PTSD checklist (PCL-5; Bellevue). The scores on these scales were used to classify symptoms into categories like non-remitting and resilient. Moreover, although the mPSS and PCL-5 are not diagnostic tools, they can be used for a provisional diagnosis for PTSD based on cut-off scores of 21 and 33, respectively.
The authors developed an algorithm to predict the progression of participants’ PTS symptoms after discharge from Grady Memorial Hospital (the model development sample). They used latent growth mixture models – a statistical technique that accounts for heterogeneity in individual trajectories – to predict symptom trajectory over time. Specifically, the mPSS was used to assess the model’s accuracy in distinguishing between (i) non-remitting symptoms and resilience, and (ii) non-remitting symptoms compared to other symptom categories. They identified predictors that significantly contributed to the model’s diagnostic accuracy and validated the algorithm in the Bellevue participant cohort (the external validation sample). Finally, they developed an algorithm to predict a provisional PTSD diagnosis outcome one year after the traumatic experience. To do this, they trained the algorithm on the development sample and tested it on the validation sample.
What did they find?
Nearly a third of the participants received a provisional diagnosis of PTSD one month after the traumatic experience. After 12 months, this percentage dropped to 15.5% and 22% in the Grady Memorial and the Bellevue cohorts, respectively. The model successfully discriminated between non-remitting and resilient trajectories with 83% accuracy in the development sample and 84% accuracy in the validation sample. Self-reported stress, immune markers, and chloride levels were the best predictors of the model’s accuracy. Moreover, the model distinguished between non-remitting and the other symptom trajectories with 96% accuracy, although accuracy dropped to 78% in the validation sample. Lastly, the model predicted a diagnosis of provisional PTSD with 87% accuracy in the validation sample. Altogether, these results demonstrate that self-reported measures of stress are predictive of whether someone will go on to exhibit non-remitting PTS symptoms and might be at risk of being diagnosed with PTSD.
What's the impact?
The authors developed a novel predictive algorithm that uses self-report and medical information collected immediately after a life-threatening traumatic experience to assess the risk of PTSD, which has important implications for performing individualized risk assessment and treatment planning for trauma survivors. In particular, the finding that psychological stress was one of the main predictors of symptom trajectory highlights the importance of collecting self-report measures of stress and PTSD symptoms in trauma centers.
Schultebraucks et al. A validated predictive algorithm of post-traumatic stress course following emergency department admission after a traumatic stressor. Nature Medicine (2020). Access the original scientific publication here.