A Validated Predictive Algorithm of Post-Traumatic Stress Course Following Emergency Department Admission After a Traumatic Stressor
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LETTERS https://doi.org/10.1038/s41591-020-0951-z A validated predictive algorithm of post-traumatic stress course following emergency department admission after a traumatic stressor Katharina Schultebraucks 1,2,3 ✉ , Arieh Y. Shalev1, Vasiliki Michopoulos4,5, Corita R. Grudzen6, Soo-Min Shin6, Jennifer S. Stevens 4, Jessica L. Maples-Keller4, Tanja Jovanovic7, George A. Bonanno8, Barbara O. Rothbaum4, Charles R. Marmar1,9, Charles B. Nemeroff10,11, Kerry J. Ressler4,12 and Isaac R. Galatzer-Levy1,13 Annually, approximately 30 million patients are discharged Notably, PTSD comes with long-term clinical and pecuniary costs from the emergency department (ED) after a traumatic to both the individual and the healthcare system. While empirically event1. These patients are at substantial psychiatric risk, validated treatments are effective in reducing the risk for PTSD6,8,9, with approximately 10–20% developing one or more disor- early prevention strategies are typically not implemented due to the ders, including anxiety, depression or post-traumatic stress lack of established methods for timely and reliable risk identifica- disorder (PTSD)2–4. At present, no accurate method exists to tion11. The ED visit is often the sole contact of trauma survivors predict the development of PTSD symptoms upon ED admis- with the healthcare system and the time immediately after trauma sion after trauma5. Accurate risk identification at the point opens a critical window to prevent the development of PTSD11,30. of treatment by ED services is necessary to inform the tar- Accurate identification of risk for PTSD during ED evaluation using geted deployment of existing treatment6–9 to mitigate sub- algorithms running on accessible data sources would provide new sequent psychopathology in high-risk populations10,11. This opportunities for cost-effective and scalable methods of risk assess- work reports the development and validation of an algorithm ment and intervention to reduce the prevalence of PTSD without for prediction of post-traumatic stress course over 12 months posing high additional burden for ED personnel11. using two independently collected prospective cohorts of The use of predictive models to integrate multiple post-traumatic trauma survivors from two level 1 emergency trauma centers, stress (PTS) risk indicators has demonstrated moderate to strong which uses routinely collectible data from electronic medical predictive accuracy on a proof-of-concept level12–14. However, the records, along with brief clinical assessments of the patient’s frequent lack of external validation in the literature obscures the immediate stress reaction. Results demonstrate externally generalizability of model performance31,32, ultimately hampering the validated accuracy to discriminate PTSD risk with high preci- implementation of such algorithms in clinical practice. Recognizing sion. While the predictive algorithm yields useful reproduc- the high clinical need, the US National Institute of Mental Health ible results on two independent prospective cohorts of ED has funded a large multi-site consortium that will start to collect patients, future research should extend the generalizability to data from independent sites33 suitable to evaluate the reliability the broad, clinically heterogeneous ED population under con- of emerging predictive models of PTS course. Independently of ditions of routine medical care. this effort, large hospital systems are actively working to identify Previous studies identified multiple trauma-related predic- novel methods that can be integrated into the standard of care to tive signals of PTSD risk7,12–17, including aspects of the biologi- improve patient outcomes and decrease long-term costs to the hos- cal stress response18–23, immune response24–26, threat perception, pital system34. Together, there is an indication of both the necessary psychophysiological arousal15,19,27 and psychosocial determinants research and clinical interest in the development and deployment of of clinical risk28. Many indicators related to these biological sys- data-driven approaches to predict the clinical risk of psychopathol- tems and psychosocial indicators are routinely collected in the ogy in the context of ED healthcare. ED and logged in the electronic medical records (EMRs), mak- We set out to develop and test the prediction of PTSD symptom ing them viable as candidate predictors of risk. Some factors, such development in a reproducible way across independent samples. At as self-reported psychological stress, are not yet part of the medi- two independent sites (Supplementary Figs. 1 and 2), ED patients cal routine and only about 7% of level 1 trauma centers routinely who reported experience of a traumatic event according to trauma screen for PTSD29. criterion A35 were enrolled in a prospective longitudinal cohort. 1Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA. 2Vagelos School of Physicians and Surgeons, Department of Emergency Medicine, Columbia University Irving Medical Center, New York, NY, USA. 3Data Science Institute, Columbia University, New York, NY, USA. 4Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA. 5Yerkes National Primate Research Center, Atlanta, GA, USA. 6Ronald O. Perelman Department of Emergency Medicine, New York University Grossman School of Medicine, New York, NY, USA. 7Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI, USA. 8Department of Counseling and Clinical Psychology, Teachers College, Columbia University, New York, NY, USA. 9Center for Alcohol Use Disorder and PTSD, New York University Grossman School of Medicine, New York, NY, USA. 10Dell Medical School, Department of Psychiatry, University of Texas at Austin, Austin, TX, USA. 11Institute for Early Life Adversity Research, University of Texas at Austin, Austin, TX, USA. 12McLean Hospital, Harvard Medical School, Boston, MA, USA. 13AiCure LLC, New York, NY, USA. ✉e-mail: [email protected] NatURE MEDICINE | www.nature.com/naturemedicine LETTERS NATURE MEDICINE Table 1 | Inclusion and exclusion criteria Model development sample (Grady Memorial) External validation sample (Bellevue Hospital) Inclusion criteria •→Adults 18–65 years of age •→Adults 18–70 years of age •→Evidence of acute traumatic event exposure defined by •→Evidence of acute traumatic event exposure defined by DSM-4 PTSD criterion A DSM-5 PTSD criterion A •→Fluency in English •→Fluency in English, Spanish or Mandarin •→have blood obtained by ED care staff •→Living in New York tri-state area Exclusion criteria •→Evidence of traumatic brain injury indicated by a Glasgow •→Evidence of traumatic brain injury indicated by a Glasgow Coma Scale score <15 Coma Scale score <13 •→Current or past history of mania, schizophrenia, other •→Evidence of present or past psychotic symptoms psychoses •→Evidence of ongoing traumatic exposure (for example, •→Prominent suicidal ideation in the last month domestic violence) •→Intoxication •→Evidence of homicidality/suicidality •→Severe pain •→Adults in police custody or Department of Correction •→Active labor patients •→Respiratory distress •→Admission to an intensive care unit, admission or surgery, •→Admission to an intensive care unit, admission or surgery, medical instability or hemodynamic medical instability or hemodynamic compromise. •→No phone or home address •→No phone or home address •→Evidence of an inability to understand study procedures, risks •→Evidence of an inability to understand study procedures, or otherwise unable to give informed consent risks or otherwise unable to give informed consent Detailed sample characteristics are presented in Supplementary Table 2 | Prediction of provisional PTSD diagnosis at 12 months Tables 1–3 and Table 1. after ED admission We first built a cross-validated predictive algorithm of non-remitting PTSD symptom progression over 12 months follow- Provisional PTSD diagnosis ing ED discharge based on a multi-layer ensemble machine-learning 1 month 6 months 12 months approach in a prospective longitudinal cohort (n = 377; Grady Memorial Hospital, Atlanta) of ED patients who had experienced Model development 32.3% (109 of 19.3% (53 of 15.5% (40 of a traumatic event (Supplementary Fig. 1). Second, we externally sample 337) 274) 258) validated the algorithm in an independent prospective longitudinal External validation 30.4% (35 of 30.7% (31 of 22% (17 of 75) cohort (n = 377; Bellevue Hospital Center, New York City) of ED sample 115) 101) patients admitted after trauma (Supplementary Fig. 2). Additionally, The number of ED patients in the sample who screened positive for provisional PTSD diagnosis we determined the predictive accuracy of distinct symptom trajec- (PTSD Checklist for DSM-5 (PCL-5) score ≥33 or modified PTSD Symptom Scale (mPSS) score tories beyond non-remitting PTSD symptoms (‘one-versus-rest’ ≥21) and number of ED patients for which available scores were available. Percentages and frequencies in brackets are based on complete case analysis of fully available PCL-5 or mPSS scores classification). Finally, we tested the prediction of provisional PTSD at the respective time of measurement. diagnosis at 12 months after ED admission (Table 2). Extended Data Fig. 1 shows a schematic overview of the study design. Heterogeneity in the symptom progression over 12 months Supplementary Table 6 and Supplementary Fig. 5). Moreover, we following ED discharge was statistically described