NYU Researchers Create Personalized PTSD Diagnosis Tool
Researchers have built a new computational tool that identifies 800 different ways people are at increased risk for post-traumatic stress disorder (PTSD), permitting for the first time a personalized prediction guide.
Researchers have built a new computational tool that identifies 800 different ways people are at increased risk for post-traumatic stress disorder (PTSD), permitting for the first time a personalized prediction guide.
Results from the study, conducted by New York University's Langone Medical Center and the Danish Veteran Center in Ringsted, Denmark, are published online in the journal BMC Psychiatry.
Current computation methods to help clinicians diagnose PTSD are capable of calculating the average risk for entire groups of survivors, not granular enough to serve as individual risk prediction tools. The new algorithm applied risk prediction tools, currently used to predict the growth of cancer, to predicting PTSD. The algorithm showed that, when applied to data collected within 10 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.
Also See: FDA Approves Defense Mobile App for Traumatic Brain Injuries
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, said study co-author Arieh Y. Shalev, M.D., professor of psychiatry at NYU Langone. Until now, we have not had a toolin this case a computational algorithmthat can weigh the many different ways in which trauma occurs to individuals and provides a personalized risk estimate.
Shalev stressed this latest publication is a proof of concept paper. For robust prediction across conditions, he says, the identified algorithm needs to be used to gather knowledge gained in traumatic events experienced by other patient populations and traumatic events.
To build a generalized predictive model, the research team, in collaboration with researchers from Columbia and Harvard universities, has already received datasets from 19 other centers worldwide in an National Institute of Mental Health-funded study designed to produce a comprehensive predictive algorithm.
The study is available here.
Results from the study, conducted by New York University's Langone Medical Center and the Danish Veteran Center in Ringsted, Denmark, are published online in the journal BMC Psychiatry.
Current computation methods to help clinicians diagnose PTSD are capable of calculating the average risk for entire groups of survivors, not granular enough to serve as individual risk prediction tools. The new algorithm applied risk prediction tools, currently used to predict the growth of cancer, to predicting PTSD. The algorithm showed that, when applied to data collected within 10 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.
Also See: FDA Approves Defense Mobile App for Traumatic Brain Injuries
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, said study co-author Arieh Y. Shalev, M.D., professor of psychiatry at NYU Langone. Until now, we have not had a toolin this case a computational algorithmthat can weigh the many different ways in which trauma occurs to individuals and provides a personalized risk estimate.
Shalev stressed this latest publication is a proof of concept paper. For robust prediction across conditions, he says, the identified algorithm needs to be used to gather knowledge gained in traumatic events experienced by other patient populations and traumatic events.
To build a generalized predictive model, the research team, in collaboration with researchers from Columbia and Harvard universities, has already received datasets from 19 other centers worldwide in an National Institute of Mental Health-funded study designed to produce a comprehensive predictive algorithm.
The study is available here.
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