Hostname: page-component-6bf8c574d5-xtvcr Total loading time: 0 Render date: 2025-02-20T12:28:47.320Z Has data issue: false hasContentIssue false
  • English
  • Français

Derivation and validation of risk prediction for posttraumatic stress symptoms following trauma exposure

Published online by Cambridge University Press:  01 July 2022

Raphael Kim ,
Tina Lin ,
Gehao Pang ,
Yufeng Liu ,
Andrew S. Tungate ,
Phyllis L. Hendry ,
Michael C. Kurz ,
David A. Peak ,
Jeffrey Jones  and
Niels K. Rathlev
...Show all authors
Raphael Kim
Affiliation:
Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC, USA
Tina Lin*
Affiliation:
Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA
Gehao Pang
Affiliation:
Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA
Yufeng Liu
Affiliation:
Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC, USA Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA Department of Genetics, Carolina Center for Genome Sciences, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA
Andrew S. Tungate
Affiliation:
Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA
Phyllis L. Hendry
Affiliation:
Department of Emergency Medicine, University of Florida College of Medicine, Jacksonville, FL, USA
Michael C. Kurz
Affiliation:
Department of Emergency Medicine, University of Alabama, Birmingham, AL, USA
David A. Peak
Affiliation:
Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
Jeffrey Jones
Affiliation:
Department of Emergency Medicine, Spectrum Health Butterworth Campus, Grand Rapids, MI, USA
Niels K. Rathlev
Affiliation:
Department of Emergency Medicine, Baystate State Health System, Springfield, MA, USA
Robert A. Swor
Affiliation:
Department of Emergency Medicine, Beaumont Hospital, Royal Oak, MI, USA
Robert Domeier
Affiliation:
Department of Emergency Medicine, St Joseph Mercy Health System, Ann Arbor, MI, USA
Marc-Anthony Velilla
Affiliation:
Department of Emergency Medicine, Sinai Grace, Detroit, MI, USA
Christopher Lewandowski
Affiliation:
Department of Emergency Medicine, Henry Ford Hospital, Detroit, MI, USA
Elizabeth Datner
Affiliation:
Department of Emergency Medicine, Albert Einstein Medical Center, Philadelphia, PA, USA
Claire Pearson
Affiliation:
Department of Emergency Medicine, Detroit Receiving, Detroit, MI, USA
David Lee
Affiliation:
Department of Emergency Medicine, North Shore University Hospital, Manhasset, NY, USA
Patricia M. Mitchell
Affiliation:
Department of Emergency Medicine, Boston University School of Medicine, Boston, MA, USA
Samuel A. McLean
Affiliation:
Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA Department of Emergency Medicine, University of North Carolina, Chapel Hill, NC, USA
Sarah D. Linnstaedt*
Affiliation:
Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA
*
Author for correspondence: Sarah D. Linnstaedt, E-mail: [email protected]
Author for correspondence: Sarah D. Linnstaedt, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Posttraumatic stress symptoms (PTSS) are common following traumatic stress exposure (TSE). Identification of individuals with PTSS risk in the early aftermath of TSE is important to enable targeted administration of preventive interventions. In this study, we used baseline survey data from two prospective cohort studies to identify the most influential predictors of substantial PTSS.

Methods

Self-identifying black and white American women and men (n = 1546) presenting to one of 16 emergency departments (EDs) within 24 h of motor vehicle collision (MVC) TSE were enrolled. Individuals with substantial PTSS (⩾33, Impact of Events Scale – Revised) 6 months after MVC were identified via follow-up questionnaire. Sociodemographic, pain, general health, event, and psychological/cognitive characteristics were collected in the ED and used in prediction modeling. Ensemble learning methods and Monte Carlo cross-validation were used for feature selection and to determine prediction accuracy. External validation was performed on a hold-out sample (30% of total sample).

Results

Twenty-five percent (n = 394) of individuals reported PTSS 6 months following MVC. Regularized linear regression was the top performing learning method. The top 30 factors together showed good reliability in predicting PTSS in the external sample (Area under the curve = 0.79 ± 0.002). Top predictors included acute pain severity, recovery expectations, socioeconomic status, self-reported race, and psychological symptoms.

Conclusions

These analyses add to a growing literature indicating that influential predictors of PTSS can be identified and risk for future PTSS estimated from characteristics easily available/assessable at the time of ED presentation following TSE.

Keywords

Machine learningpredictionPTSDtraumarisk factors
Type
Original Article
Information
Psychological Medicine , Volume 53 , Issue 11 , August 2023 , pp. 4952 - 4961
Check for updates
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

These authors contributed equally to this work.

†

Co-senior.

References

Adams, Z. W., Sumner, J. A., Danielson, C. K., McCauley, J. L., Resnick, H. S., Grös, K., … Ruggiero, K. J. (2014). Prevalence and predictors of PTSD and depression among adolescent victims of the Spring 2011 tornado outbreak. Journal of Child Psychology and Psychiatry, 55(9), 1047–1055. doi: 10.1111/jcpp.12220CrossRefGoogle ScholarPubMed
Adler, N. E., & Stead, W. W. (2015). Patients in context – EHR capture of social and behavioral determinants of health. The New England Journal of Medicine, 372(8), 698–701. doi: 10.1056/NEJMp1413945CrossRefGoogle ScholarPubMed
Alegría, M., Fortuna, L. R., Lin, J. Y., Norris, F. H., Gao, S., Takeuchi, D. T., … Valentine, A. (2013). Prevalence, risk, and correlates of posttraumatic stress disorder across ethnic and racial minority groups in the United States. Medical Care, 51(12), 1114–1123. doi: 10.1097/mlr.0000000000000007CrossRefGoogle ScholarPubMed
Ali, N., Neagu, D., & Trundle, P. (2019). Evaluation of k-nearest neighbour classifier performance for heterogeneous data sets. Springer Nature Applied Sciences, 1(12), 1559. doi: 10.1007/s42452-019-1356-9Google Scholar
Antoniadi, A. M., Galvin, M., Heverin, M., Hardiman, O., & Mooney, C. (2021). Prediction of caregiver quality of life in amyotrophic lateral sclerosis using explainable machine learning. Scientific Reports, 11(1), 12237. doi: 10.1038/s41598-021-91632-2CrossRefGoogle ScholarPubMed
Austin, E., Pan, W., & Shen, X. (2013). Penalized regression and risk prediction in genome-wide association studies. Statistical Analysis and Data Mining, 6(4), 315–328. doi: 10.1002/sam.11183CrossRefGoogle ScholarPubMed
Bayramli, I., Castro, V., Barak-Corren, Y., Madsen, E. M., Nock, M. K., Smoller, J. W., & Reis, B. Y. (2021). Temporally informed random forests for suicide risk prediction. Journal of the American Medical Informatics Association, 29(1), 62–71. doi: 10.1093/jamia/ocab225CrossRefGoogle ScholarPubMed
Bishop, C. M. (1995). Neural networks for pattern recognition. New York, NY: Oxford University Press, Inc.Google Scholar
Bleich, A., & Solomon, Z. (2004). Evaluation of psychiatric disability in PTSD of military origin. The Israel Journal of Psychiatry and Related Sciences, 41(4), 268–276.Google ScholarPubMed
Brennstuhl, M. J., Tarquinio, C., & Montel, S. (2015). Chronic pain and PTSD: Evolving views on their comorbidity. Perspectives in Psychiatric Care, 51(4), 295–304. doi: 10.1111/ppc.12093CrossRefGoogle ScholarPubMed
Brooks Holliday, S., Dubowitz, T., Haas, A., Ghosh-Dastidar, B., DeSantis, A., & Troxel, W. M. (2020). The association between discrimination and PTSD in African Americans: Exploring the role of gender. Ethnicity & Health, 25(5), 717–731. doi: 10.1080/13557858.2018.1444150CrossRefGoogle ScholarPubMed
Byun, H., & Lee, S.-W. (2002). Applications of support vector machines for pattern recognition: a survey. Paper presented at the Pattern Recognition with Support Vector Machines, Berlin, Heidelberg.CrossRefGoogle Scholar
Chan, A. W., Pristach, E. A., Welte, J. W., & Russell, M. (1993). Use of the TWEAK test in screening for alcoholism/heavy drinking in three populations. Alcoholism, Clinical and Experimental Research, 17(6), 1188–1192. doi: 10.1111/j.1530-0277.1993.tb05226.xCrossRefGoogle ScholarPubMed
Chen, X., & Ishwaran, H. (2012). Random forests for genomic data analysis. Genomics, 99(6), 323–329. doi: 10.1016/j.ygeno.2012.04.003CrossRefGoogle ScholarPubMed
Creamer, M., Bell, R., & Failla, S. (2003). Psychometric properties of the impact of event scale – revised. Behaviour Research and Therapy, 41(12), 1489–1496. doi: 10.1016/j.brat.2003.07.010CrossRefGoogle ScholarPubMed
Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems, 2(4), 303–314. doi: 10.1007/BF02551274CrossRefGoogle Scholar
Defrin, R., Ginzburg, K., Solomon, Z., Polad, E., Bloch, M., Govezensky, M., & Schreiber, S. (2008). Quantitative testing of pain perception in subjects with PTSD – implications for the mechanism of the coexistence between PTSD and chronic pain. Pain, 138(2), 450–459. doi: 10.1016/j.pain.2008.05.006CrossRefGoogle ScholarPubMed
de Vlaming, R., & Groenen, P. J. (2015). The current and future use of ridge regression for prediction in quantitative genetics. BioMed Research International, 2015, 143712. doi: 10.1155/2015/143712CrossRefGoogle ScholarPubMed
Do, D. P., Diez Roux, A. V., Hajat, A., Auchincloss, A. H., Merkin, S. S., Ranjit, N., … Seeman, T. (2011). Circadian rhythm of cortisol and neighborhood characteristics in a population-based sample: The multi-ethnic study of atherosclerosis. Health & Place, 17(2), 625–632. doi: 10.1016/j.healthplace.2010.12.019CrossRefGoogle Scholar
Dobie, D. J., Kivlahan, D. R., Maynard, C., Bush, K. R., Davis, T. M., & Bradley, K. A. (2004). Posttraumatic stress disorder in female veterans: Association with self-reported health problems and functional impairment. Archives of Internal Medicine, 164(4), 394–400. doi: 10.1001/archinte.164.4.394CrossRefGoogle ScholarPubMed
Eastel, J. M., Lam, K. W., Lee, N. L., Lok, W. Y., Tsang, A. H. F., Pei, X. M., … Wong, S. C. C. (2019). Application of NanoString technologies in companion diagnostic development. Expert Review of Molecular Diagnostics, 19(7), 591–598. doi: 10.1080/14737159.2019.1623672CrossRefGoogle ScholarPubMed
Ehring, T., Razik, S., & Emmelkamp, P. M. (2011). Prevalence and predictors of posttraumatic stress disorder, anxiety, depression, and burnout in Pakistani earthquake recovery workers. Psychiatry Research, 185(1–2), 161–166. doi: 10.1016/j.psychres.2009.10.018CrossRefGoogle ScholarPubMed
Feinberg, R. K., Hu, J., Weaver, M. A., Fillingim, R. B., Swor, R. A., Peak, D. A., … McLean, S. A. (2017). Stress-related psychological symptoms contribute to axial pain persistence after motor vehicle collision: Path analysis results from a prospective longitudinal study. Pain, 158(4), 682–690. doi: 10.1097/j.pain.0000000000000818CrossRefGoogle ScholarPubMed
Freedman, S. A., Brandes, D., Peri, T., & Shalev, A. Y. (1999). Predictors of chronic post-traumatic stress disorder. A prospective study. The British Journal of Psychiatry, 174(4), 353–359. doi: 10.1192/bjp.174.4.353CrossRefGoogle ScholarPubMed
Fritz, J. M., Magel, J. S., McFadden, M., Asche, C., Thackeray, A., Meier, W., & Brennan, G. (2015). Early physical therapy vs usual care in patients with recent-onset low back pain: A randomized clinical trial. JAMA, 314(14), 1459–1467. doi: 10.1001/jama.2015.11648CrossRefGoogle ScholarPubMed
Galatzer-Levy, I. R., Karstoft, K.-I., Statnikov, A., & Shalev, A. Y. (2014). Quantitative forecasting of PTSD from early trauma responses: A machine learning application. Journal of Psychiatric Research, 59, 68–76. doi: 10.1016/j.jpsychires.2014.08.017CrossRefGoogle ScholarPubMed
Gallego, A.-J., Pertusa, A., & Calvo-Zaragoza, J. (2018). Improving convolutional neural networks’ accuracy in noisy environments using k-nearest neighbors. Applied Sciences, 8(11), 2086. Retrieved from https://www.mdpi.com/2076-3417/8/11/2086.CrossRefGoogle Scholar
Gaskin, D. J., & Richard, P. (2012). The economic costs of pain in the United States. The Journal of Pain, 13(8), 715–724. doi: 10.1016/j.jpain.2012.03.009CrossRefGoogle ScholarPubMed
Georgoulas, G., Stylios, C. D., & Groumpos, P. P. (2006). Predicting the risk of metabolic acidosis for newborns based on fetal heart rate signal classification using support vector machines. IEEE Transactions on Biomedical Engineering, 53(5), 875–884. doi: 10.1109/TBME.2006.872814CrossRefGoogle ScholarPubMed
Gruber, S., Krakower, D., Menchaca, J. T., Hsu, K., Hawrusik, R., Maro, J. C., … Klompas, M. (2020). Using electronic health records to identify candidates for human immunodeficiency virus pre-exposure prophylaxis: An application of super learning to risk prediction when the outcome is rare. Statistics in Medicine, 39(23), 3059–3073. doi: 10.1002/sim.8591CrossRefGoogle ScholarPubMed
Haskell, S. G., Gordon, K. S., Mattocks, K., Duggal, M., Erdos, J., Justice, A., & Brandt, C. A. (2010). Gender differences in rates of depression, PTSD, pain, obesity, and military sexual trauma among Connecticut War Veterans of Iraq and Afghanistan. Journal of Women's Health, 19(2), 267–271. doi: 10.1089/jwh.2008.1262CrossRefGoogle ScholarPubMed
Holbrook, T. L., Galarneau, M. R., Dye, J. L., Quinn, K., & Dougherty, A. L. (2010). Morphine use after combat injury in Iraq and post-traumatic stress disorder. The New England Journal of Medicine, 362(2), 110–117. doi: 10.1056/NEJMoa0903326CrossRefGoogle ScholarPubMed
Hu, C., & Steingrimsson, J. A. (2018). Personalized risk prediction in clinical oncology research: Applications and practical issues using survival trees and random forests. Journal of Biopharmaceutical Statistics, 28(2), 333–349. doi: 10.1080/10543406.2017.1377730CrossRefGoogle ScholarPubMed
Johansen, V. A., Wahl, A. K., Eilertsen, D. E., & Weisaeth, L. (2007). Prevalence and predictors of post-traumatic stress disorder (PTSD) in physically injured victims of non-domestic violence. A longitudinal study. Social Psychiatry and Psychiatric Epidemiology, 42(7), 583–593. doi: 10.1007/s00127-007-0205-0CrossRefGoogle ScholarPubMed
Karb, R. A., Elliott, M. R., Dowd, J. B., & Morenoff, J. D. (2012). Neighborhood-level stressors, social support, and diurnal patterns of cortisol: The Chicago Community Adult Health Study. Social Science & Medicine, 75(6), 1038–1047. doi: 10.1016/j.socscimed.2012.03.031CrossRefGoogle ScholarPubMed
Karstoft, K.-I., Galatzer-Levy, I. R., Statnikov, A., Li, Z., Shalev, A. Y., & For Members of the Jerusalem Trauma Outreach and Prevention Study. (2015a). Bridging a translational gap: Using machine learning to improve the prediction of PTSD. BMC Psychiatry, 15(1), 30. doi: 10.1186/s12888-015-0399-8CrossRefGoogle ScholarPubMed
Karstoft, K.-I., Statnikov, A., Andersen, S. B., Madsen, T., & Galatzer-Levy, I. R. (2015b). Early identification of posttraumatic stress following military deployment: Application of machine learning methods to a prospective study of Danish soldiers. Journal of Affective Disorders, 184, 170–175. doi: 10.1016/j.jad.2015.05.057CrossRefGoogle ScholarPubMed
Kearns, M. C., Ressler, K. J., Zatzick, D., & Rothbaum, B. O. (2012). Early interventions for PTSD: A review. Depression and Anxiety, 29(10), 833–842. doi: 10.1002/da.21997CrossRefGoogle ScholarPubMed
Kessler, R. C. (2000). Posttraumatic stress disorder: The burden to the individual and to society. The Journal of Clinical Psychiatry, 61 (Suppl 5), 4–12; discussion 13–14.Google Scholar
Kessler, R. C., Hwang, I., Hoffmire, C. A., McCarthy, J. F., Petukhova, M. V., Rosellini, A. J., … Bossarte, R. M. (2017). Developing a practical suicide risk prediction model for targeting high-risk patients in the Veterans health Administration. International Journal of Methods in Psychiatric Research, 26(3). doi: 10.1002/mpr.1575CrossRefGoogle ScholarPubMed
Kessler, R. C., Rose, S., Koenen, K. C., Karam, E. G., Stang, P. E., Stein, D. J., … McLaughlin, K. A. (2014). How well can post-traumatic stress disorder be predicted from pre-trauma risk factors? An exploratory study in the WHO World Mental Health Surveys. World Psychiatry, 13(3), 265–274. doi: 10.1002/wps.20150CrossRefGoogle ScholarPubMed
Khaylis, A., Waelde, L., & Bruce, E. (2007). The role of ethnic identity in the relationship of race-related stress to PTSD symptoms among young adults. Journal of Trauma & Dissociation, 8(4), 91–105. doi: 10.1300/J229v08n04_06CrossRefGoogle ScholarPubMed
Kilpatrick, D. G., Resnick, H. S., Milanak, M. E., Miller, M. W., Keyes, K. M., & Friedman, M. J. (2013). National estimates of exposure to traumatic events and PTSD prevalence using DSM-IV and DSM-5 criteria. Journal of Traumatic Stress, 26(5), 537–547. doi: 10.1002/jts.21848CrossRefGoogle ScholarPubMed
Kim, S. K., Yoo, T. K., Oh, E., & Kim, D. W. (2013). Osteoporosis risk prediction using machine learning and conventional methods. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013, 188–191. doi: 10.1109/EMBC.2013.6609469CrossRefGoogle Scholar
Kind, A. J., & Buckingham, W. R. (2018). Making neighborhood-disadvantage metrics accessible – The neighborhood atlas. The New England Journal of Medicine, 378(26), 2456–2458. doi: 10.1056/NEJMp1802313CrossRefGoogle ScholarPubMed
Kleim, B., Ehlers, A., & Glucksman, E. (2007). Early predictors of chronic post-traumatic stress disorder in assault survivors. Psychological Medicine, 37(10), 1457–1467. doi: 10.1017/S0033291707001006CrossRefGoogle ScholarPubMed
Kramer, O. (2013). K-nearest neighbors. In Kramer, O. (Ed.), Dimensionality reduction with unsupervised nearest neighbors (pp. 13–23). Berlin, Heidelberg: Springer Berlin Heidelberg.CrossRefGoogle Scholar
Kuhn, M., & Johnson, K. (2013). Data pre-processing. In Applied predictive modeling (pp. 27–59). New York, NY: Springer.CrossRefGoogle Scholar
Lew, H. L., Otis, J. D., Tun, C., Kerns, R. D., Clark, M. E., & Cifu, D. X. (2009). Prevalence of chronic pain, posttraumatic stress disorder, and persistent postconcussive symptoms in OIF/OEF veterans: Polytrauma clinical triad. Journal of Rehabilitation Research & Development, 46(6), 697–702. doi: 10.1682/jrrd.2009.01.0006CrossRefGoogle ScholarPubMed
Li, C., Zhang, S., Zhang, H., Pang, L., Lam, K., Hui, C., & Zhang, S. (2012). Using the K-nearest neighbor algorithm for the classification of lymph node metastasis in gastric cancer. Computational and Mathematical Methods in Medicine, 2012, 876545. doi: 10.1155/2012/876545CrossRefGoogle ScholarPubMed
Linnstaedt, S. D., Hu, J., Liu, A. Y., Soward, A. C., Bollen, K. A., Wang, H. E., … Velilla, M.-A. (2016). Methodology of AA CRASH: A prospective observational study evaluating the incidence and pathogenesis of adverse post-traumatic sequelae in African-Americans experiencing motor vehicle collision. BMJ Open, 6(9), e012222. doi: 10.1136/bmjopen-2016-012222CrossRefGoogle ScholarPubMed
Linnstaedt, S. D., Rueckeis, C. A., Riker, K. D., Pan, Y., Wu, A., Yu, S., … McLean, S. A. (2019a). microRNA-19b predicts widespread pain and posttraumatic stress symptom risk in a sex-dependent manner following trauma exposure. Pain, 161(1), 47–60. doi: 10.1097/j.pain.0000000000001709CrossRefGoogle Scholar
Linnstaedt, S. D., Zannas, A. S., McLean, S. A., Koenen, K. C., & Ressler, K. J. (2019b). Literature review and methodological considerations for understanding circulating risk biomarkers following trauma exposure. Molecular Psychiatry, 25(9), 1986–1999. doi: 10.1038/s41380-019-0636-5CrossRefGoogle ScholarPubMed
Litz, B. T., Gray, M. J., Bryant, R. A., & Adler, A. B. (2002). Early intervention for trauma: Current status and future directions. Clinical Psychology: Science and Practice, 9(2), 112–134. doi: 10.1093/clipsy.9.2.112Google Scholar
Lund, J. L., Kuo, T. M., Brookhart, M. A., Meyer, A. M., Dalton, A. F., Kistler, C. E., … Lewis, C. L. (2019). Development and validation of a 5-year mortality prediction model using regularized regression and Medicare data. Pharmacoepidemiology and Drug Safety, 28(5), 584–592. doi: 10.1002/pds.4769CrossRefGoogle ScholarPubMed
Maddoux, J., McFarlane, J., Symes, L., Fredland, N., & Feder, G. (2018). Using baseline data to predict chronic PTSD 48-months after mothers report intimate partner violence: Outcomes for mothers and the intergenerational impact on child behavioral functioning. Archives of Psychiatric Nursing, 32(3), 475–482. doi: 10.1016/j.apnu.2018.02.001CrossRefGoogle ScholarPubMed
Marafino, B. J., Boscardin, W. J., & Dudley, R. A. (2015). Efficient and sparse feature selection for biomedical text classification via the elastic net: Application to ICU risk stratification from nursing notes. Journal of Biomedical Informatics, 54, 114–120. doi: 10.1016/j.jbi.2015.02.003CrossRefGoogle ScholarPubMed
McLean, S. A., Clauw, D. J., Abelson, J. L., & Liberzon, I. (2005). The development of persistent pain and psychological morbidity after motor vehicle collision: Integrating the potential role of stress response systems into a biopsychosocial model. Psychosomatic Medicine, 67(5), 783–790. doi: 10.1097/01.psy.0000181276.49204.bbCrossRefGoogle ScholarPubMed
McLean, S. A., Ressler, K., Koenen, K. C., Neylan, T., Germine, L., Jovanovic, T., … Kessler, R. (2019). The AURORA study: A longitudinal, multimodal library of brain biology and function after traumatic stress exposure. Molecular Psychiatry, 25(2), 283–296. doi: 10.1038/s41380-019-0581-3CrossRefGoogle ScholarPubMed
McNally, R. J., & Frueh, B. C. (2013). Why are Iraq and Afghanistan War veterans seeking PTSD disability compensation at unprecedented rates? Journal of Anxiety Disorders, 27(5), 520–526. doi: 10.1016/j.janxdis.2013.07.002CrossRefGoogle ScholarPubMed
Mittag, F., Büchel, F., Saad, M., Jahn, A., Schulte, C., Bochdanovits, Z., … Sharma, M. (2012). Use of support vector machines for disease risk prediction in genome-wide association studies: Concerns and opportunities. Human Mutation, 33(12), 1708–1718. doi: 10.1002/humu.22161CrossRefGoogle ScholarPubMed
Nash, V. R., Ponto, J., Townsend, C., Nelson, P., & Bretz, M. N. (2013). Cognitive behavioral therapy, self-efficacy, and depression in persons with chronic pain. Pain Management Nursing, 14(4), e236–e243. doi: 10.1016/j.pmn.2012.02.006CrossRefGoogle ScholarPubMed
Odgers, D. J., Tellis, N., Hall, H., & Dumontier, M. (2016). Using LASSO Regression to predict rheumatoid arthritis treatment efficacy. AMIA Joint Summits on Translational Science Proceedings, 2016, 176–183. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/27570666Google ScholarPubMed
Outcalt, S. D., Kroenke, K., Krebs, E. E., Chumbler, N. R., Wu, J., Yu, Z., & Bair, M. J. (2015). Chronic pain and comorbid mental health conditions: Independent associations of posttraumatic stress disorder and depression with pain, disability, and quality of life. Journal of Behavioral Medicine, 38(3), 535–543. doi: 10.1007/s10865-015-9628-3CrossRefGoogle ScholarPubMed
Parker, J. S., Mullins, M., Cheang, M. C., Leung, S., Voduc, D., Vickery, T., … Hu, Z. (2009). Supervised risk predictor of breast cancer based on intrinsic subtypes. Journal of Clinical Oncology, 27(8), 1160–1167. doi: 10.1200/JCO.2008.18.1370CrossRefGoogle ScholarPubMed
Pavlou, M., Ambler, G., Seaman, S., De Iorio, M., & Omar, R. Z. (2016). Review and evaluation of penalised regression methods for risk prediction in low-dimensional data with few events. Statistics in Medicine, 35(7), 1159–1177. doi: 10.1002/sim.6782CrossRefGoogle ScholarPubMed
Petersen, M. L., LeDell, E., Schwab, J., Sarovar, V., Gross, R., Reynolds, N., … Bangsberg, D. R. (2015). Super learner analysis of electronic adherence data improves viral prediction and may provide strategies for selective HIV RNA monitoring. Journal of Acquired Immune Deficiency Syndrome, 69(1), 109–118. doi: 10.1097/QAI.0000000000000548CrossRefGoogle ScholarPubMed
Platts-Mills, T. F., Ballina, L., Bortsov, A. V., Soward, A., Swor, R. A., Jones, J. S., … Rathlev, N. K. (2011). Using emergency department-based inception cohorts to determine genetic characteristics associated with long term patient outcomes after motor vehicle collision: Methodology of the CRASH study. BMC Emergency Medicine, 11(1), 14. doi: 10.1186/1471-227X-11-14CrossRefGoogle ScholarPubMed
Polley, E. C., & Van Der Laan, M. J. (2010). Super Learner in prediction. U.C. Berkeley Division of Biostatistics Working Paper Series, Working Paper 266. Retrieved from https://biostats.bepress.com/ucbbiostat/paper266.Google Scholar
Powers, M. B., Warren, A. M., Rosenfield, D., Roden-Foreman, K., Bennett, M., Reynolds, M. C., … Smits, J. A. (2014). Predictors of PTSD symptoms in adults admitted to a Level I trauma center: A prospective analysis. Journal of Anxiety Disorders, 28(3), 301–309. doi: 10.1016/j.janxdis.2014.01.003CrossRefGoogle Scholar
Privé, F., Aschard, H., & Blum, M. G. B. (2019). Efficient implementation of penalized regression for genetic risk prediction. Genetics, 212(1), 65–74. doi: 10.1534/genetics.119.302019CrossRefGoogle ScholarPubMed
Rosellini, A. J., Dussaillant, F., Zubizarreta, J. R., Kessler, R. C., & Rose, S. (2018). Predicting posttraumatic stress disorder following a natural disaster. Journal of Psychiatric Research, 96, 15–22. doi: 10.1016/j.jpsychires.2017.09.010CrossRefGoogle ScholarPubMed
Saifi, S., & Mehmood, T. (2011). Effects of socioeconomic status on student's achievement. International Journal of Social Sciences and Education, 1(2), 119–128.Google Scholar
Saxe, G., Stoddard, F., Courtney, D., Cunningham, K., Chawla, N., Sheridan, R., … King, L. (2001). Relationship between acute morphine and the course of PTSD in children with burns. Journal of the American Academy of Child & Adolescent Psychiatry, 40(8), 915–921. doi: 10.1097/00004583-200108000-00013CrossRefGoogle ScholarPubMed
Schultebraucks, K., Shalev, A. Y., Michopoulos, V., Grudzen, C. R., Shin, S.-M., Stevens, J. S., … Galatzer-Levy, I. R. (2020). A validated predictive algorithm of post-traumatic stress course following emergency department admission after a traumatic stressor. Nature Medicine, 26(7), 1084–1088. doi: 10.1038/s41591-020-0951-zCrossRefGoogle ScholarPubMed
Shahar, D., Shai, I., Vardi, H., Shahar, A., & Fraser, D. (2005). Diet and eating habits in high and low socioeconomic groups. Nutrition, 21(5), 559–566. doi: 10.1016/j.nut.2004.09.018CrossRefGoogle ScholarPubMed
Shalev, A. Y., Ankri, Y., Gilad, M., Israeli-Shalev, Y., Adessky, R., Qian, M., & Freedman, S. (2016). Long-term outcome of early interventions to prevent posttraumatic stress disorder. The Journal of Clinical Psychiatry, 77(5), e580–e587. doi: 10.4088/JCP.15m09932CrossRefGoogle ScholarPubMed
Shalev, A. Y., Gevonden, M., Ratanatharathorn, A., Laska, E., van der Mei, W. F., Qi, W., … International Consortium to Predict PTSD (2019). Estimating the risk of PTSD in recent trauma survivors: Results of the International Consortium to Predict PTSD (ICPP). World Psychiatry, 18(1), 77–87. doi: 10.1002/wps.20608CrossRefGoogle Scholar
Short, N. A., Tungate, A. S., Bollen, K. A., Sullivan, J., D'Anza, T., Lechner, M., … McLean, S. A. (2022). Pain is common after sexual assault and posttraumatic arousal/reactivity symptoms mediate the development of new or worsening persistent pain. Pain, 163(1), e121–e128. doi: 10.1097/j.pain.0000000000002329CrossRefGoogle ScholarPubMed
Shouman, M., Turner, T., & Stocker, R. (2012). Applying k-nearest neighbour in diagnosing heart disease patients. International Journal of Information and Education Technology, 2(3), 220–223. doi: 10.7763/IJIET.2012.V2.114CrossRefGoogle Scholar
Steinberg, D. M., Fine, J., & Chappell, R. (2009). Sample size for positive and negative predictive value in diagnostic research using case-control designs. Biostatistics, 10(1), 94–105. doi: 10.1093/biostatistics/kxn018CrossRefGoogle ScholarPubMed
Stekhoven, D. J., & Bühlmann, P. (2012). MissForest – non-parametric missing value imputation for mixed-type data. Bioinformatics, 28(1), 112–118. doi: 10.1093/bioinformatics/btr597CrossRefGoogle ScholarPubMed
Stewart, W. F., Ricci, J. A., Chee, E., Hahn, S. R., & Morganstein, D. (2003). Cost of lost productive work time among US workers with depression. JAMA, 289(23), 3135–3144. doi: 10.1001/jama.289.23.3135CrossRefGoogle ScholarPubMed
Surís, A., & Lind, L. (2008). Military sexual trauma: A review of prevalence and associated health consequences in veterans. Trauma, Violence, & Abuse, 9(4), 250–269. doi: 10.1177/1524838008324419CrossRefGoogle ScholarPubMed
Symes, L., Maddoux, J., McFarlane, J., & Pennings, J. (2016). A risk assessment tool to predict sustained PTSD symptoms among women reporting abuse. Journal of Women's Health, 25(4), 340–347. doi: 10.1089/jwh.2015.5287CrossRefGoogle ScholarPubMed
Torquati, M., Mendis, M., Xu, H., Myneni, A. A., Noyes, K., Hoffman, A. B., … Becerra, A. Z. (2022). Using the Super Learner algorithm to predict risk of 30-day readmission after bariatric surgery in the United States. Surgery, 171(3), 621–627. doi: 10.1016/j.surg.2021.06.019CrossRefGoogle ScholarPubMed
Vergunst, F., Tremblay, R. E., Nagin, D., Algan, Y., Beasley, E., Park, J., … Côté, S. M. (2019). Association of behavior in boys from low socioeconomic neighborhoods with employment earnings in adulthood. JAMA Pediatrics, 173(4), 334–341. doi: 10.1001/jamapediatrics.2018.5375CrossRefGoogle ScholarPubMed
Wongvibulsin, S., Wu, K. C., & Zeger, S. L. (2019). Clinical risk prediction with random forests for survival, longitudinal, and multivariate (RF-SLAM) data analysis. BMC Medical Research Methodology, 20(1), 1. doi: 10.1186/s12874-019-0863-0CrossRefGoogle ScholarPubMed
Wyss, R., Schneeweiss, S., van der Laan, M., Lendle, S. D., Ju, C., & Franklin, J. M. (2018). Using super learner prediction modeling to improve high-dimensional propensity score estimation. Epidemiology, 29(1), 96–106. doi: 10.1097/ede.0000000000000762CrossRefGoogle ScholarPubMed
Xing, W., & Bei, Y. (2020). Medical health big data classification based on KNN classification algorithm. IEEE Access, 8, 28808–28819. doi: 10.1109/ACCESS.2019.2955754CrossRefGoogle Scholar
Yokota, S., Endo, M., & Ohe, K. (2017). Establishing a classification system for high fall-risk among inpatients using support vector machines. Computers Informatics Nursing, 35(8), 408–416. doi: 10.1097/CIN.0000000000000332CrossRefGoogle ScholarPubMed
Zhuang, J., Cai, J., Wang, R., Zhang, J., & Zheng, W.-S. (2020). Deep kNN for medical image classification. Paper presented at the Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, Cham.CrossRefGoogle Scholar
Ziobrowski, H. N., Kennedy, C. J., Ustun, B., House, S. L., Beaudoin, F. L., An, X., … van Rooij, S. J. H. (2021). Development and validation of a model to predict posttraumatic stress disorder and major depression after a motor vehicle collision. JAMA Psychiatry, 78(11), 1228–1237. doi: 10.1001/jamapsychiatry.2021.2427CrossRefGoogle Scholar
Zlomuzica, A., Preusser, F., Schneider, S., & Margraf, J. (2015). Increased perceived self-efficacy facilitates the extinction of fear in healthy participants. Frontiers in Behavioral Neuroscience, 9, 270. doi: 10.3389/fnbeh.2015.00270CrossRefGoogle ScholarPubMed
Supplementary material: File

Kim et al. supplementary material

Kim et al. supplementary material

Download Kim et al. supplementary material(File)
File 454.1 KB