Algorithm predicts risk for PTSD after traumatic injury
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Researchers have developed an algorithm that can predict whether trauma survivors are likely to develop posttraumatic stress disorder (PTSD). The tool, which relies on routinely collected medical data, would allow clinicians to intervene early to mitigate the effects of PTSD.

Numerous biological and psychological biomarkers—including elevated stress hormones, increased inflammatory signals, high blood pressure, and hyperarousal (an abnormally heightened state of anxiety) - often precede PTSD in trauma survivors. However, none of these measures, alone or in combination, has proved reliable at predicting PTSD.

In the new study, the multi-site research team used supervised machine learning to develop an algorithm that computes a single PTSD risk score from a combination of 70 clinical data points and a brief clinical assessment of a patient's immediate stress response. (Supervised machine learning is a form of artificial intelligence that gives a computer system the ability to recognize patterns from data inputs to make predictions about new observations without additional programming.)

The researchers developed the algorithm with data from 377 adult trauma survivors in Atlanta and then tested the algorithm in 221 adult trauma survivors in New York City.

Among patients who were categorized by the algorithm as PTSD risks, 90% developed long-lasting PTSD symptoms within a year. Only 5% of patients who were free of long-lasting PTSD symptoms had been identified as at risk. Of the patients predicted to have no or few PTSD symptoms, 29% developed long-lasting PTSD (false negatives).

"Currently only 7% of level-1 trauma centers routinely screen for PTSD, We hope that the algorithm will provide ED clinicians with a rapid, automatic readout that they could use for discharge planning and the prevention of PTSD", said researchers.