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Algorithm predicts PTSD risk within 10 days of trauma

Researchers have developed a computational tool that can identify early risk factors for posttraumatic stress disorder within 10 days of a traumatic event, according to data.

“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,” Arieh Y. Shalev, MD, the Barbara Wilson Professor in the department of psychiatry at NYU Langone and a co-director of NYU’s Steven and Alexandra Cohen Veterans Center, said in a press release. “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.”

Researchers assessed event characteristics, emergency department (ED) records and early symptoms for 957 trauma survivors within 10 days of their ED admission, and tracked their PTSD symptoms during the preceding 15 months.

The average number of Markov Boundaries was measured as 800; the mean area under the curve (AUC) was 0.75 (95% CI, 0.67-0.8), according to data.

Data also indicate the average number of features that contributed to the prediction of symptoms were 18 (range: 12-32), and 13 features evident in over 75% of the algorithm sets.

“Until recently, we mainly used early symptoms to predict PTSD, and it had its drawbacks,” Shalev said in the release. “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.”– by Samantha Costa

Disclosure: The researchers report no relevant financial disclosures.