Reader TTS

ABSTRACT Annually, approximately 30 million patients are discharged from the emergency department (ED) after a traumatic event1. These patients are at substantial psychiatric risk, with

approximately 10–20% developing one or more disorders, including anxiety, depression or post-traumatic stress disorder (PTSD)2,3,4. At present, no accurate method exists to predict the

development of PTSD symptoms upon ED admission after trauma5. Accurate risk identification at the point of treatment by ED services is necessary to inform the targeted deployment of existing

treatment6,7,8,9 to mitigate subsequent psychopathology in high-risk populations10,11. This work reports the development and validation of an algorithm for prediction of post-traumatic

stress course over 12 months using two independently collected prospective cohorts of trauma survivors from two level 1 emergency trauma centers, which uses routinely collectible data from

electronic medical records, along with brief clinical assessments of the patient’s immediate stress reaction. Results demonstrate externally validated accuracy to discriminate PTSD risk with

high precision. While the predictive algorithm yields useful reproducible results on two independent prospective cohorts of ED patients, future research should extend the generalizability

to the broad, clinically heterogeneous ED population under conditions of routine medical care. Access through your institution Buy or subscribe This is a preview of subscription content,

access via your institution ACCESS OPTIONS Access through your institution Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription $32.99

/ 30 days cancel any time Learn more Subscribe to this journal Receive 12 print issues and online access $209.00 per year only $17.42 per issue Learn more Buy this article * Purchase on

SpringerLink * Instant access to full article PDF Buy now Prices may be subject to local taxes which are calculated during checkout ADDITIONAL ACCESS OPTIONS: * Log in * Learn about

institutional subscriptions * Read our FAQs * Contact customer support SIMILAR CONTENT BEING VIEWED BY OTHERS A SYSTEMATIC REVIEW OF MACHINE LEARNING FINDINGS IN PTSD AND THEIR RELATIONSHIPS

WITH THEORETICAL MODELS Article 07 January 2025 A 20-YEAR LONGITUDINAL COHORT STUDY OF POST-TRAUMATIC STRESS DISORDER IN WORLD TRADE CENTER RESPONDERS Article 27 May 2025 DEVELOPMENT AND

VALIDATION OF A RISK PREDICTION MODEL FOR POST-TRAUMATIC STRESS DISORDER AMONG CHINESE BREAST CANCER SURVIVORS Article Open access 17 March 2025 DATA AVAILABILITY All requests for raw and

analyzed data and related materials, including programming code, will be reviewed by our legal departments (New York University Grossman School of Medicine and Emory University School of

Medicine) to verify whether the request is subject to any intellectual property or confidentiality constraints. Any data and materials that can be shared will be released via a material

transfer agreement for noncommercial research purposes. Request should be addressed to the corresponding author (K.S.) or the Principal Investigators of the two study sites (K.J.R. and

I.R.G.-L.). CODE AVAILABILITY The programming code is based on Scikit-learn (https://scikit-learn.org/stable/) and SHAP (https://github.com/slundberg/shap) and the core algorithm can be

obtained from https://github.com/KSchultebraucks/DeepSuperLearner. Requests should be addressed to the corresponding author (K.S.). REFERENCES * DiMaggio, C. J., Avraham, J. B., Lee, D. C.,

Frangos, S. G. & Wall, S. P. The epidemiology of emergency department trauma discharges in the United States. _Acad. Emerg. Med._ 24, 1244–1256 (2017). PubMed  PubMed Central  Google

Scholar  * Wiseman, T. A., Curtis, K., Lam, M. & Foster, K. Incidence of depression, anxiety and stress following traumatic injury: a longitudinal study. _Scand. J. Trauma Resusc. Emerg.

Med._ 23, 29 (2015). PubMed  PubMed Central  Google Scholar  * Sullivan, E. et al. The association between posttraumatic stress symptoms, depression, and length of hospital stay following

traumatic injury. _Gen. Hosp. Psychiatry_ 46, 49–54 (2017). PubMed  Google Scholar  * Fakhry, S. M. et al. Continuing trauma: the unmet needs of trauma patients in the postacute care

setting. _Am. Surgeon_ 83, 1308–1314 (2017). PubMed  Google Scholar  * Shalev, A. Y. et al. Estimating the risk of PTSD in recent trauma survivors: results of the International Consortium to

Predict PTSD (ICPP). _World Psychiatry_ 18, 77–87 (2019). PubMed  PubMed Central  Google Scholar  * Rothbaum, B. O. et al. Early intervention following trauma may mitigate genetic risk for

PTSD in civilians: a pilot prospective emergency department study. _J. Clin. Psychiat._ 75, 1380 (2014). Google Scholar  * Galatzer-Levy, I. R. et al. Early PTSD symptom trajectories:

persistence, recovery, and response to treatment: results from the Jerusalem Trauma Outreach and Prevention Study (J-TOPS). _PLoS ONE_ 8, e70084 (2013). CAS  PubMed  PubMed Central  Google

Scholar  * Shalev, A. Y. et al. Long-term outcome of early interventions to prevent posttraumatic stress disorder. _J. Clin. Psychiat._ 77, e580–e587 (2016). Google Scholar  * Shalev, A. Y.

et al. Prevention of posttraumatic stress disorder by early treatment: results from the Jerusalem Trauma Outreach And Prevention study. _Arch. Gen. Psychiatry_ 69, 166–176 (2012). PubMed 

Google Scholar  * Roberts, N. P., Kitchiner, N. J., Kenardy, J. & Bisson, J. I. Early psychological interventions to treat acute traumatic stress symptoms. _Cochrane Database Syst. Rev._

17, CD007944 (2010). Google Scholar  * Shalev, A. Y. & Barbano, A. C. PTSD: risk assessment and early management. _Psychiatr. Ann._ 49, 299–306 (2019). Google Scholar  * Galatzer-Levy,

I. R., Karstoft, K. I., Statnikov, A. & Shalev, A. Y. Quantitative forecasting of PTSD from early trauma responses: a machine-learning application. _J. Psychiatr. Res._ 59, 68–76 (2014).

PubMed  PubMed Central  Google Scholar  * Galatzer-Levy, I. R., Ma, S., Statnikov, A., Yehuda, R. & Shalev, A. Y. Utilization of machine learning for prediction of post-traumatic

stress: a re-examination of cortisol in the prediction and pathways to non-remitting PTSD. _Transl. Psychiatry_ 7, e1070 (2017). CAS  PubMed Central  Google Scholar  * Karstoft, K.-I.,

Galatzer-Levy, I. R., Statnikov, A., Li, Z. & Shalev, A. Y. Bridging a translational gap: using machine learning to improve the prediction of PTSD. _BMC Psychiatry_ 15, 30 (2015). PubMed

  PubMed Central  Google Scholar  * Shalev, A. Y. et al. A prospective study of heart rate response following trauma and the subsequent development of posttraumatic stress disorder. _Arch.

Gen. Psychiatry_ 55, 553–559 (1998). CAS  PubMed  Google Scholar  * Yehuda, R., McFarlane, A. & Shalev, A. Predicting the development of posttraumatic stress disorder from the acute

response to a traumatic event. _Biol. Psychiatry_ 44, 1305–1313 (1998). CAS  PubMed  Google Scholar  * Papini, S. et al. Ensemble machine-learning prediction of posttraumatic stress disorder

screening status after emergency room hospitalization. _J. Anxiety Disord._ 60, 35–42 (2018). PubMed  PubMed Central  Google Scholar  * Ressler, K. J. Molecular signatures of stress and

posttraumatic stress disorder: an overview. _Biol. Psychiatry_ 83, 792–794 (2018). PubMed  Google Scholar  * Hinrichs, R. et al. Increased skin conductance response in the immediate

aftermath of trauma predicts PTSD risk. _Chronic Stress_ 3, 2470547019844441 (2019). PubMed Central  Google Scholar  * Heim, C., Schultebraucks, K., Marmar, C. R. & Nemeroff, C. B. in

_Post‐Traumatic Stress Disorder_ (eds Nemeroff, C. B. & Marmar, C.) 331 (Oxford Univ. Press, 2018). * Morris, M. C., Hellman, N., Abelson, J. L. & Rao, U. Cortisol, heart rate, and

blood pressure as early markers of PTSD risk: a systematic review and meta-analysis. _Clin. Psychol. Rev._ 49, 79–91 (2016). PubMed  PubMed Central  Google Scholar  * Van Zuiden, M. et al.

Glucocorticoid receptor pathway components predict posttraumatic stress disorder symptom development: a prospective study. _Biol. Psychiatry_ 71, 309–316 (2012). PubMed  Google Scholar  *

Schultebraucks, K. et al. Heightened biological stress response during exposure to a trauma film predicts an increase in intrusive memories. _J. Abnorm. Psychol._ 128, 645 (2019). PubMed 

Google Scholar  * Michopoulos, V. et al. Association of prospective risk for chronic PTSD symptoms with low TNF-α and IFN-γ concentrations in the immediate aftermath of trauma exposure. _Am.

J. Psychiatry_ 2019, 19010039 (2019). Google Scholar  * Mellon, S. H., Gautam, A., Hammamieh, R., Jett, M. & Wolkowitz, O. M. Metabolism, metabolomics, and inflammation in posttraumatic

stress disorder. _Biol. Psychiatry_ 83, 866–875 (2018). CAS  PubMed  Google Scholar  * Michopoulos, V., Powers, A., Gillespie, C. F., Ressler, K. J. & Jovanovic, T. Inflammation in

fear-and anxiety-based disorders: PTSD, GAD, and beyond. _Neuropsychopharmacology_ 42, 254 (2017). CAS  PubMed  Google Scholar  * Nugent, N. R., Christopher, N. C. & Delahanty, D. L.

Emergency medical service and in-hospital vital signs as predictors of subsequent PTSD symptom severity in pediatric injury patients. _J. Child Psychol. Psychiatry_ 47, 919–926 (2006).

PubMed  Google Scholar  * Shalev, A., Liberzon, I. & Marmar, C. Post-traumatic stress disorder. _New Engl. J. Med._ 376, 2459–2469 (2017). PubMed  Google Scholar  * Love, J. &

Zatzick, D. Screening and intervention for comorbid substance disorders, PTSD, depression, and suicide: a trauma center survey. _Psychiatr. Serv._ 65, 918–923 (2014). PubMed  PubMed Central

  Google Scholar  * Vermetten, E., Zhohar, J. & Krugers, H. J. Pharmacotherapy in the aftermath of trauma; opportunities in the ‘golden hours’. _Curr. Psychiatry Rep._ 16, 455 (2014).

PubMed  Google Scholar  * Altman, D. G., Vergouwe, Y., Royston, P. & Moons, K. G. Prognosis and prognostic research: validating a prognostic model. _Br. Med. J._ 338, b605 (2009). Google

Scholar  * Schultebraucks, K. & Galatzer-Levy, I. R. Machine learning for prediction of posttraumatic stress and resilience following trauma: an overview of basic concepts and recent

advances. _J. Trauma Stress_ 32, 215–225 (2019). PubMed  Google Scholar  * McLean, S. A. et al. The AURORA study: a longitudinal, multimodal library of brain biology and function after

traumatic stress exposure. _Mol. Psychiatry_ 25, 283–296 (2020). PubMed  Google Scholar  * Horwitz, L. I., Kuznetsova, M. & Jones, S. A. Creating a learning health system through

rapid-cycle, randomized testing. _New Engl. J. Med._ 381, 1175 (2019). PubMed  Google Scholar  * American Psychiatric Association. _Diagnostic and Statistical Manual of Mental Disorders

(DSM-5)_ (American Psychiatric Publishing, 2013). * Fein, J. A., Kassam-Adams, N., Vu, T. & Datner, E. M. Emergency department evaluation of acute stress disorder symptoms in violently

injured youths. _Ann. Emerg. Med._ 38, 391–396 (2001). CAS  PubMed  Google Scholar  * Marmar, C. R, Weiss, D. S. & Metzler, T. J. The Peritraumatic Dissociative Experiences

Questionnaire. in _Assessing Psychological Trauma and PTSD_ 2nd edn (eds Wilson, J. P. & Kean, T. M.) 144–167 (Guilford Press, 2004). * Lundberg, S. M. & Lee, S.-I. A unified

approach to interpreting model predictions. _Adv. Neural Inf. Process. Syst._ 30, 4765–4774 (2017). Google Scholar  * Blevins, C. A., Weathers, F. W., Davis, M. T., Witte, T. K. &

Domino, J. L. The posttraumatic stress disorder checklist for DSM‐5 (PCL‐5): development and initial psychometric evaluation. _J. Trauma. Stress_ 28, 489–498 (2015). PubMed  Google Scholar 

* Schultebraucks, K., Wen, T., Kronish, I. M., Willey, J. & Chang, B. P. Post-traumatic stress disorder following acute stroke. _Curr. Emerg. Hosp. Med. Rep._ 8, 1–8 (2020). Google

Scholar  * World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. _J. Am. Med. Assoc._ 310, 2191

(2013). * Foa, E. B., Riggs, D. S., Dancu, C. V. & Rothbaum, B. O. Reliability and validity of a brief instrument for assessing post‐traumatic stress disorder. _J. Trauma. Stress_ 6,

459–473 (1993). Google Scholar  * Falsetti, S. A., Resnick, H. S., Resick, P. A. & Kilpatrick, D. G. The modified PTSD symptom scale: a brief self-report measure of posttraumatic stress

disorder. _Behav. Ther._ 16, 161–162 (1993). Google Scholar  * Weathers, F. W., et al. The PTSD checklist for DSM-5 (PCL-5). _Department of Verterans Affairs_ http://www.ptsd.va.gov (2013).

* Ruglass, L. M., Papini, S., Trub, L. & Hien, D. A. Psychometric properties of the modified posttraumatic stress disorder symptom scale among women with posttraumatic stress disorder

and substance use disorders receiving outpatient group treatments. _J. Trauma. Stress Disord. Treat._ https://doi.org/10.4172/2324-8947.1000139 (2014). * Weathers, F. W. Redefining

posttraumatic stress disorder for DSM-5. _Curr. Opin. Psychol._ 14, 122–126 (2017). PubMed  Google Scholar  * Muthén, L. K. & Muthén, B. O. _Mplus User’s Guide: Statistical Analysis with

Latent Variables_ (Muthén & Muthén, 1998–2017). * van de Schoot, R., Sijbrandij, M., Winter, S. D., Depaoli, S. & Vermunt, J. K. The GRoLTS-checklist: guidelines for reporting on

latent trajectory studies. _Struct. Equ. Modeling_ 24, 451–467 (2017). Google Scholar  * Kuhn, M. & Johnson, K. _Applied Predictive Modeling_ (Springer, 2013). * Young, S., Abdou, T.

& Bener, A. Deep Super Learner: a deep ensemble for classification problems. in _Advances in Artificial Intelligence. Canadian Conference on Artificial Intelligence (Canadian AI 2018)_

(eds Bagheri, E. & Cheung, J. C. K.) 84–95 (2018). * Pedregosa, F. et al. Scikit-learn: machine learning in Python. _J. Mach. Learn. Res._ 12, 2825–2830 (2011). Google Scholar  *

Breiman, L. Random forests. _Mach. Learn._ 45, 5–32 (2001). Google Scholar  * Hastie, T., Tibshirani, R. & Friedman, J. _The Elements of Statistical Learning_, (Springer, 2001). * Ferri,

C., Hernández-Orallo, J. & Modroiu, R. An experimental comparison of performance measures for classification. _Pattern Recognit. Lett._ 30, 27–38 (2009). Google Scholar  * Zhou, Z.-H.

& Feng, J. Deep forest. _Nat. Sci. Rev._ 6, 74–86 (2018). Google Scholar  * Fawcett, T. ROC graphs: notes and practical considerations for researchers. _Mach. Learn._ 31, 1–38 (2004).

Google Scholar  * Collins, G. S., Reitsma, J. B., Altman, D. G. & Moons, K. G. M. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis

(TRIPOD): the TRIPOD statement. _BMC Med._ 13, 1 (2015). PubMed  PubMed Central  Google Scholar  * Luo, W. et al. Guidelines for developing and reporting machine learning predictive models

in biomedical research: a multidisciplinary view. _J. Med. Internet Res._ 18, e323 (2016). PubMed  PubMed Central  Google Scholar  * Moons, K. G. M., Royston, P., Vergouwe, Y., Grobbee, D.

E. & Altman, D. Prognosis and prognostic research: what, why, and how? _Br. Med. J._ 338, b605 (2009). Google Scholar  * Štrumbelj, E. & Kononenko, I. Explaining prediction models

and individual predictions with feature contributions. _Knowl. Inf. Syst._ 41, 647–665 (2014). Google Scholar  Download references ACKNOWLEDGEMENTS K.S. was supported by the German Research

Foundation (SCHU 3259/1–1). The study was also supported by K01MH102415 (I.R.G.-L.), R01MH094759 (C.B.N.) and R01MH094757 (K.J.R.). AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department

of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA Katharina Schultebraucks, Arieh Y. Shalev, Charles R. Marmar & Isaac R. Galatzer-Levy * Vagelos School

of Physicians and Surgeons, Department of Emergency Medicine, Columbia University Irving Medical Center, New York, NY, USA Katharina Schultebraucks * Data Science Institute, Columbia

University, New York, NY, USA Katharina Schultebraucks * Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA Vasiliki Michopoulos, 

Jennifer S. Stevens, Jessica L. Maples-Keller, Barbara O. Rothbaum & Kerry J. Ressler * Yerkes National Primate Research Center, Atlanta, GA, USA Vasiliki Michopoulos * Ronald O.

Perelman Department of Emergency Medicine, New York University Grossman School of Medicine, New York, NY, USA Corita R. Grudzen & Soo-Min Shin * Department of Psychiatry and Behavioral

Neurosciences, Wayne State University, Detroit, MI, USA Tanja Jovanovic * Department of Counseling and Clinical Psychology, Teachers College, Columbia University, New York, NY, USA George A.

Bonanno * Center for Alcohol Use Disorder and PTSD, New York University Grossman School of Medicine, New York, NY, USA Charles R. Marmar * Dell Medical School, Department of Psychiatry,

University of Texas at Austin, Austin, TX, USA Charles B. Nemeroff * Institute for Early Life Adversity Research, University of Texas at Austin, Austin, TX, USA Charles B. Nemeroff * McLean

Hospital, Harvard Medical School, Boston, MA, USA Kerry J. Ressler * AiCure LLC, New York, NY, USA Isaac R. Galatzer-Levy Authors * Katharina Schultebraucks View author publications You can

also search for this author inPubMed Google Scholar * Arieh Y. Shalev View author publications You can also search for this author inPubMed Google Scholar * Vasiliki Michopoulos View author

publications You can also search for this author inPubMed Google Scholar * Corita R. Grudzen View author publications You can also search for this author inPubMed Google Scholar * Soo-Min

Shin View author publications You can also search for this author inPubMed Google Scholar * Jennifer S. Stevens View author publications You can also search for this author inPubMed Google

Scholar * Jessica L. Maples-Keller View author publications You can also search for this author inPubMed Google Scholar * Tanja Jovanovic View author publications You can also search for

this author inPubMed Google Scholar * George A. Bonanno View author publications You can also search for this author inPubMed Google Scholar * Barbara O. Rothbaum View author publications

You can also search for this author inPubMed Google Scholar * Charles R. Marmar View author publications You can also search for this author inPubMed Google Scholar * Charles B. Nemeroff

View author publications You can also search for this author inPubMed Google Scholar * Kerry J. Ressler View author publications You can also search for this author inPubMed Google Scholar *

Isaac R. Galatzer-Levy View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS I.R.G.-L., K.J.R, C.B.N., A.Y.S., C.R.M., B.O.R., V.M., T.J. and

K.S. substantially contributed to the design of the study and developed the study concept. S.-M.S., I.R.G.-L., V.M., J.S.S., J.L.M.-K., C.R.G. were involved in the data collection process.

K.S. developed the data analytical plan and performed data analysis. G.A.B. and I.R.G.-L. provided supervision. K.S. wrote the first draft of the manuscript and all co-authors reviewed and

revised the manuscript critically for important intellectual content. All co-authors approved the version of the manuscript to be published. CORRESPONDING AUTHOR Correspondence to Katharina

Schultebraucks. ETHICS DECLARATIONS COMPETING INTERESTS B.O.R. has funding from Wounded Warrior Project, Department of Defense Clinical Trial Grant No.W81XWH-10-1-1045, National Institute of

Mental Health grant no. 1R01MH094757-01 and McCormick Foundation. B.O.R. also receives royalties from Oxford University Press, Guilford, APPI and Emory University and received advisory

board payments from Genentech, Jazz Pharmaceuticals, Sophren, Nobilis Therapeutics, Neuronetics and Aptinyx. C.R.M. serves on the scientific advisory board and has equity in Receptor Life

Sciences. He also serves on the PTSD advisory board for Otsuka Pharmaceutical. He receives support from the National Institute on Alcohol Abuse and Alcoholism, National Institute of Mental

Health, Department of Defense, US Army Congressionally Directed Medical Research Program, the Steven & Alexander Cohen Foundation, Cohen Veterans Bioscience, Cohen Veterans Network, Home

Depot Foundation, McCormick Foundation, Robin Hood Foundation and the City of New York. C.B.N. discloses the following: research/grants from National Institutes of Health and Stanley

Medical Research Institute; consulting (last three years) at Xhale, Takeda, Taisho Pharmaceutical Inc., Bracket (Clintara), Fortress Biotech, Sunovion Pharmaceuticals Inc., Sumitomo

Dainippon Pharma, Janssen Research & Development LLC, Magstim, Inc., Navitor Pharmaceuticals, Inc., TC MSO, Inc. and Intra-Cellular Therapies, Inc.; stockholder of Xhale, Celgene,

Seattle Genetics, Abbvie, OPKO Health, Inc., Antares, BI Gen Holdings, Inc., Corcept Therapeutics Pharmaceuticals Company, TC MSO, Inc. and Trends in Pharma Development, LLC; scientific

advisory boards of American Foundation for Suicide Prevention (AFSP), Brain and Behavior Research Foundation, Xhale, Anxiety Disorders Association of America (ADAA), Skyland Trail, Bracket

(Clintara) and Laureate Institute for Brain Research Inc.; board of directors of AFSP, Gratitude America, ADAA and Xhale Smart, Inc.; income sources or equity of USD$10,000 or more from

American Psychiatric Publishing, Xhale, Bracket (Clintara), CME Outfitters, Takeda, Intra-Cellular Therapies, Inc., Magstim and EMA Wellness; patents for the method and devices for

transdermal delivery of lithium (US 6,375,990B1), the method of assessing antidepressant drug therapy via transport inhibition of monoamine neurotransmitters by ex vivo assay (US

7,148,027B2) and compounds, compositions, methods of synthesis and methods of treatment (CRF receptor binding ligand) (US 8,551,996 B2). K.J.R. performs consulting for Janssen, Verily,

Alkermes and Biogen, Inc. on matters unrelated to this manuscript. I.R.G.-L. receives salary and stock options from AiCure. All other authors declare no competing interests. ADDITIONAL

INFORMATION PEER REVIEW INFORMATION Kate Gao was the primary editor on this article, and managed its editorial process and peer review in collaboration with the rest of the editorial team.

PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. EXTENDED DATA EXTENDED DATA FIG. 1 SCHEMATIC OVERVIEW

OF THE STUDY DESIGN. Displayed are the basic steps of the model development and model validation. EXTENDED DATA FIG. 2 PREDICTIVE PERFORMANCE IN TERMS OF DISCRIMINATION AND CALIBRATION. In

panel A, the ROC curve shows the specificity and the sensitivity of the predictions on the training set (blue line) and the external validation set (orange line) and is accompanied by a

calibration plot for the predicted probabilities on the training set (orange line and blue bars) and the external validation set (red line and green bars) in panel B. The bars in the

calibration plot in panel (B) displays the predicted probabilities in 10 bins [0, 10%], (10%, 20%],…, (90%, 100%], whereas the lines visualize the predicted probabilities in two bins (low

vs. high probability). SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION Supplementary Figs. 1–7, Supplementary Tables 1–10, Supplementary Discussion. REPORTING SUMMARY RIGHTS AND

PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Schultebraucks, K., Shalev, A.Y., Michopoulos, V. _et al._ A validated predictive algorithm of post-traumatic stress

course following emergency department admission after a traumatic stressor. _Nat Med_ 26, 1084–1088 (2020). https://doi.org/10.1038/s41591-020-0951-z Download citation * Received: 16 July

2019 * Accepted: 22 May 2020 * Published: 06 July 2020 * Issue Date: July 2020 * DOI: https://doi.org/10.1038/s41591-020-0951-z SHARE THIS ARTICLE Anyone you share the following link with

will be able to read this content: Get shareable link Sorry, a shareable link is not currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt

content-sharing initiative