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Posttraumatic stress disorder (PTSD) often develops after traumatic events, with a lifetime prevalence of 7.8% in the Netherlands. Only the first weeks post-trauma provide a unique window of opportunity for preventive interventions to reduce prevalence of long-term PTSD, related adverse outcomes and societal costs, including mental health care use. Importantly, these interventions are only beneficial if delivered as indicated preventive intervention to individuals at high risk for long-term PTSD. Existing prognostic screening instruments, however, fail to adequately predict long-term PTSD when applied in the first weeks post-trauma. This is because they cannot sufficiently capture PTSD’s heterogeneous course and etiology.

 

The main outcome of this project will be a validated prognostic screening instrument that accurately predicts which recently trauma-exposed individuals are at risk for long-term PTSD (primary outcome, either chronic or delayed) and related adverse outcomes (secondary outcomes: wellbeing, daily functioning, quality of life, health care use, productivity and related costs). For this we will use machine learning: a data-analytic framework using data-driven modeling to find computational algorithms to recognize patterns in complex interrelated data.

We expect that the developed prognostic screening instrument results in more accurate classification of individuals at risk for chronic or delayed PTSD and related adverse outcomes at 1 year post-trauma than currently available screeners. Hereby we can thus target indicated preventive interventions to individuals who are most in need of help and will benefit, thereby preventing major suffering and adverse outcome. We will also deliver an indicated preventive intervention to recently trauma-exposed individuals at risk for long-term PTSD. This study will be the very first to assess effectiveness of an indicated preventive intervention for PTSD in an accurately detected high-risk group.

 

Adequately powered long-term prospective cohorts and appropriate data analytic methods are vital to more fully comprehend the complex and dynamic PTSD course and etiology and to attain accurate early PTSD risk detection. Therefore, this project has the following build-up: First, we will increase understanding of etiological mechanisms of PTSD, by prospectively investigating long-term PTSD symptom course (1-15 years post-trauma) and associated risk and protective factors in existing acute injury cohorts (ICPP: N=3049, TraumaTips: N=726, WP1) and a new diverse cohort of acute injury, accident and crime victims (N=652, WP2). By adding a new follow-up assessment to the TraumaTips cohort we will obtain a unique follow-up at 12-15 years post-trauma (WP1). Next, we will derive (WP3) and validate (WP4) a self-report prognostic screening instrument for individual risk classification for long-term PTSD generalizable to a diverse recently trauma-exposed population, using the existing (TraumaTips) and new cohort respectively. As females have a 1.5 to 2-fold higher risk for PTSD than males, we also explore whether sex interacts with PTSD course and identified risk and protective factors and whether sex-specific screening instruments improve early risk detection. By innovatively structurally addressing sex, we expect to further increase our understanding of etiological mechanisms of PTSD and improve early detection of PTSD risk. Lastly we will perform an RCT (N=60, WP5) to assess (cost-)effectiveness of an indicated preventive intervention to reduce adverse outcome in individuals detected to be at risk for chronic or delayed PTSD using the developed screening instrument. The investigated intervention, SUPPORT Coach, is an already existing self-guided mobile application developed to self-manage trauma-related symptoms using psycho-education, self-assessment, and cognitive behavioral therapy-based exercises. It was previously found beneficial in PTSD patients, but is not yet investigated as indicated preventive intervention for PTSD.

 

The proposed project is feasible, as our consortium include

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