An algorithm has been developed by researchers that has the ability to predict a person’s risk of developing post-traumatic stress disorder (PTSD) based on 800 different factors.
Researchers from NYU Langone Medical Center in New York City say the new tool will provide a more personalized assessment of PTSD risk. Current methods only allow to predict average risk for a large group of survivors.
In their study, the researchers used data from the Jerusalem Trauma Outreach and Prevention Study involving more then 4,700 participants who were admitted to emergency departments following potentially traumatic events. They then applied risk prediction tools already used in predicting cancer risk to PTSD.
Data put into the algorithm included type of event, early symptoms, and assessment from the emergency department. When used on data collected within ten days of a traumatic event, the algorithm was able to more accurately predict who is most likely to develop PTSD, even accounting for the many ways in which traumatic events occur, the researchers reported in BMC Psychiatry.
The study “shows that features like the occurrence of head trauma, duration of stay in the emergency department, or survivors' expressing a need for help, can be integrated into a predictive tool and improve the prediction,” Arieh Y. Shalev, MD, of NYU Langone said in a statement.
A new computational tool has been developed by researchers that can identify 800 different ways a person can be at risk from developing post-traumatic stress disorder (PTSD).
The results of the study, published in BMC Psychiatry, could allow for a personalized post-traumatic stress disorder (PTSD) prediction guide for the first time.
Prior to the study, clinicians have been able to calculate the average risk of PTSD for large groups of survivors. These computational methods have previously been found to be insufficient for calculating individual risk, however.