Probabilistic Risk Modeling for Disorder Post Traumatic Stress (PTSD) After the Attacks in Paris In November 2015

Stress Disorders, Post-Traumatic

The scientific approach of this project proposes to build a cohort of psychological injuries post-attack of 13 November 2015. This is called patients involved, ie patients who directly witnessed the events and having the most either came under automatic gunfire or were close to an explosion, whether or not physically injured. The main objective is to define and validate a model of transition from acute stress disorder (ASD) to posttraumatic stress disorder (PTSD) for better detection of the risk of unfavorable and thus better patient supported. The first step is to determine independent risk factors found in analytic prospective cohort study. Bayesian models are well suited to exercise because they can integrate (i) the context related to the patient, (ii) the context related to the trauma itself, and (iii) the immediate reactions and long-term to the latter . They are particularly suited to understanding the brain disordered by saying down information (prior probability) wrong by improper memory of the traumatic event. The confrontation of these descending information and perceived bottom-information could be partly responsible for the symptoms of PTSD. Once validated, the model will characterize the individual level the most at risk of unfavorable patients (calculating a probability of developing PTSD) and to assess the epidemiological impact on long-term cohort of event considered. These determinations are necessary prerequisites to optimize the means of support for these current and future casualties. The investigators main objective is to define a Bayesian model describing a resilient functioning facing a trauma which also allows to describe a possible evolution of the ASD to PTSD by an error. The state of PTSD or not will be determined by the PCL-S scale.

cohort tracking study

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