Prediction of Co-Morbid Chronic Pain and Posttraumatic Stress: Results of a Pilot Analysis of Clinical and MicroRNA Data From a Longitudinal Cohort of African American Trauma Survivors

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Conference Proceeding

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Biological Psychiatry


Background: Co-morbid chronic musculoskeletal pain and posttraumatic stress (CMSP/PTS) is a common outcome of trauma exposure and is associated with greater disability than either outcome alone. Identification of CMSP/PTS vulnerable individuals would aid in preventative treatment decisions. In the current study, we performed analyses to identify significant predictors and build a prediction tool for CMSP/PTS based on clinical and biological data. Methods: African American men/women presenting to the emergency department (ED) within 24 hours of motor vehicle collision were enrolled. Sociodemographic and psychological/cognitive characteristics, and blood (PAXgeneRNA) for microRNA-seq were collected in the ED. Six-month surveys identified individuals with CMSP (≥4, 0-10 Numeric Rating Scale)/PTS (≥33, Impact of Events Scale-Revised). The prediction tool was built using regularized logistic regression with feature selection, where significant predictors were identified via 1,000x repetitions of Monte Carlo cross-validation. Results: 30% (n=222/741) of the full cohort reported CMSP/PTS and 27% (n=198/741) reported neither outcome. Clinical and demographic variables were identified using a subset of individuals without miRNA data (n=332); selected variables showed good reliability in predicting CMSP/PTS (AUC=0.76+/-0.008). miRNA data alone (n=88) yielded weak reliability (AUC=0.64+/-0.009). Combining clinical, demographic, and miRNA variables (n=88) improved prediction versus either subset alone (AUC=0.79+/-0.008). Top predictors included initial pain severity, fear of pain getting worse, feeling frustrated or angry, socioeconomic status and microRNAs miR-199a, miR-339, let-7d, miR-192, and miR-29. Conclusions: These analyses suggest that supplementing clinical prediction with microRNA moderately improves accuracy of identifying vulnerable individuals. Future studies should aim to replicate these findings in additional trauma cohorts.


Supported By: K01AR071504, R01AR060852, The Mayday Fund Keywords: PTSD, Chronic Pain, Machine Learning, Cross-Validation, microRNA





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