206 Development and Validation of a Simple Clinical Prediction Tool to Identify Individuals in the Emergency Department at High Risk of Posttraumatic Stress Disorder After Motor Vehicle Collision

Abstract

1.4%) patients, UA in 132 (0.4%) patients, and 30,665 (98.2%) with non-ACS etiologies. After applying feature engineering and selection techniques, we arrived at 11 features in our analysis. The features found to be independently associated with NSTEMI included: systolic blood pressure, brain natriuretic peptide, congestive heart failure, coronary artery disease, creatinine, glucose, prior myocardial infarction, heart rate, nephrotic syndrome, red cell distribution width, and troponin. To address the low event rates, we incorporated the use of undersampling technique and cost-sensitive learning. Table 1 demonstrates a system engineering approach indicating the range of continuous variables most associated with NSTEMI. This model had the following test characteristics for the diagnosis of NSTEMI: sensitivity 81%, specificity 80%, and an AUC score of 0.89.Conclusion: In this study, we developed a Random Forest model with modest accuracy at predicting NSTEMI. Further work is ongoing to refine modeling with additions of electrocardiograms and clinical notes.