Investigators have created a software model that can accurately identify 800 different ways people are at increased risk for post-traumatic stress disorder (PTSD).
Researchers from New York University Langone Medical Center believe the new model will permit, for the first time, a personalized prediction guide for PTSD.
Study results have been published in the journal BMC Psychiatry.
“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 Arieh Y. Shalev, M.D., the Barbara Wilson Professor in the Department of Psychiatry at New York University Langone.
“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 provides a personalized risk estimate.”
Historically, clinicians have been limited by computation methods that were only capable of calculating the average risk for entire groups of survivors. And those have proven to be insufficient as an individual risk prediction tool.
The new algorithm applied risk prediction tools currently used to predict the growth of cancer, to predicting PTSD.
Researchers designed the study to uncover interchangeable, maximally predictive sets of early risk indicators and build a new algorithm using a model previously developed at the New York University Center for Health Bioinformatics for molecular and cancer research.
The tool showed that, when applied to data collected within ten days of a traumatic event, it can more accurately predict who is likely to develop PTSD despite the many ways in which traumatic events occur.
Data crunched into the algorithm includes variables on type of event, early symptoms, and emergency department findings.
“Until recently, we mainly used early symptoms to predict PTSD, and it had its drawbacks,” Shalev said.
“This study extends our ability to predict effectively. 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.”
Devising a strong predictive model also is imperative for tailoring prevention efforts for people at risk for developing PTSD, Shalev adds.
Shalev’s latest study builds on data originally gathered from the Jerusalem Trauma Outreach and Prevention Study, which he and colleagues conducted at Hadassah Hospital in Israel and which previously was published in Archives of General Psychiatry.
Shalev warned, though, that this publication is a “proof of concept” paper. For robust prediction across conditions, he said, the identified algorithm needs to be used to gather knowledge gained in traumatic events experienced by other patient populations and traumatic events, beyond those analyzed from the earlier study.
To build a generalized predictive model, the research team has already received datasets from 19 other centers worldwide in a study, funded by the National Institute of Mental Health, designed to produce a comprehensive predictive algorithm. It is being conducted in collaboration with researchers from Columbia and Harvard universities,
“In the future, we hope that we will be better able to tailor treatment approaches based on more personalized risk assessment,” Shalev said. “PTSD exacts a heavy toll on affected individuals and society.”
New studies in the U.S. and through the World Health Organization suggests the majority of living adults will experience at least one traumatic event during their lifetime. Moreover, five to ten percent of those exposed to traumatic events may develop PTSD.