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.
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.
“Our study shows that high-risk individuals who have experienced a traumatic event can be identified less than two weeks after they are first seen in the emergency department,” says Dr. Arieh Y. Shalev.
“Until now, we have not had a tool – in this case, a computational algorithm – that can weigh the many different ways in which trauma occurs to individuals and provide a personalized risk estimate.”
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.
“Together, current findings suggest that PTSD is associated with an array of multimodal risk indicators, many of which are observable shortly after trauma exposure. Despite these findings, research to date has failed to reveal clinically useful, personalized predictors,” write the authors.
PTSD is caused by exposure to a traumatic event, be it experienced in person or simply witnessed. This mental health condition can lead to flashbacks, nightmares and anxiety, and many people who develop the condition find that it can severely disrupt their everyday functioning.
Epidemiological studies conducted in the US and by the World Health Organization (WHO) suggest that the majority of living adults will experience a traumatic event at some point in their lives. It is estimated that around 5-10% of these people will go on to develop PTSD as a result.
The aim of the new study was to find predictive sets of early risk indicators that could be used to construct an algorithm similar to one previously developed for molecular and cancer research. The researchers used data from the Jerusalem Trauma Outreach and Prevention Study, for 4,743 participants admitted to emergency departments following potentially traumatic events.
When applied to data collected within 10 days of a traumatic event, the algorithm demonstrated that it could more accurately predict which individuals were most likely to develop PTSD, even taking into account the huge variety of ways in which traumatic events can occur.
“This study extends our ability to predict effectively,” states Dr. Shalev. “For example, it 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.”
Dr. Shalev states that the study is merely a “proof of concept,” and that further testing is required to refine the algorithm. He states that it will need to be used with more data collected from other patient populations and traumatic events in order for it to be able to provide robust predictions in all circumstances.
The National Institute of Mental Health (NIMH) is currently funding further research from the authors designed to produce a comprehensive predictive algorithm. Datasets from 19 other centers located across the world have already been received.
“In the future, we hope that we will be better able to tailor treatment approaches based on more personalized risk assessment,” states Dr. Shalev. “PTSD exacts a heavy toll on affected individuals and society.”
Recently, Medical News Today reported on a study of US Marine blood samples identifying genetic markers associated with PTSD that are also linked to the immune system response.