Machine learning model effectively predicts PTSD after motor vehicle accidents, JAMA study.

Each patient received a baseline ED assessment along with follow-up self-report surveys 2 weeks, 8 weeks, and 3 months later. An ensemble machine learning method was used to predict 3-month PTSD or MDE from baseline information. The PTSD Checklist for DSM-5 was used to assess PTSD and the Patient Reported Outcomes Measurement Information System Depression Short-Form 8b to assess MDE.

Out of 1003 patients that were included in this study, a total of 274 patients (weighted 26.6%) met criteria for 3-month PTSD or MDE. An ensemble machine learning model restricted to 30 predictors estimated in a training sample had good prediction accuracy and calibration; in an independent test sample.

Patients in the top 30% of predicted risk accounted for 65% of all 3-month PTSD or MDE, with a mean (SE) positive predictive value of 58.2% (6.4%) among these patients at high risk. The most important predictors in terms of Shapley Additive Explanations values were symptoms of anxiety sensitivity and depressive disposition, psychological distress in the 30 days before motor vehicle collision, and peritraumatic psychosomatic symptoms.

This study found that a parsimonious model that predicts 3-month PTSD or MDE after MVC can be developed using a battery of questions that could be delivered in approximately 10 minutes. The model had good AUC and calibration and captured close to two-thirds of all patients who developed 3-month PTSD or MDE in the top 30% of the predicted risk distribution.

These results suggest that if cost-effective preventive interventions are developed, identification of patients in the ED who are at high risk for treatment targeting may be possible.

Source: JAMA Psychiatry: doi:10.1001/jamapsychiatry.2021.2427