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Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors
Active-duty Army personnel can be exposed to traumatic warzone events and are at increased risk for developing post-traumatic stress disorder (PTSD) compared with the general population. PTSD is associated with high individual and societal costs, but identification of predictive markers to determine...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8589682/ https://www.ncbi.nlm.nih.gov/pubmed/32488126 http://dx.doi.org/10.1038/s41380-020-0789-2 |
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author | Schultebraucks, Katharina Qian, Meng Abu-Amara, Duna Dean, Kelsey Laska, Eugene Siegel, Carole Gautam, Aarti Guffanti, Guia Hammamieh, Rasha Misganaw, Burook Mellon, Synthia H. Wolkowitz, Owen M. Blessing, Esther M. Etkin, Amit Ressler, Kerry J. Doyle, Francis J. Jett, Marti Marmar, Charles R. |
author_facet | Schultebraucks, Katharina Qian, Meng Abu-Amara, Duna Dean, Kelsey Laska, Eugene Siegel, Carole Gautam, Aarti Guffanti, Guia Hammamieh, Rasha Misganaw, Burook Mellon, Synthia H. Wolkowitz, Owen M. Blessing, Esther M. Etkin, Amit Ressler, Kerry J. Doyle, Francis J. Jett, Marti Marmar, Charles R. |
author_sort | Schultebraucks, Katharina |
collection | PubMed |
description | Active-duty Army personnel can be exposed to traumatic warzone events and are at increased risk for developing post-traumatic stress disorder (PTSD) compared with the general population. PTSD is associated with high individual and societal costs, but identification of predictive markers to determine deployment readiness and risk mitigation strategies is not well understood. This prospective longitudinal naturalistic cohort study—the Fort Campbell Cohort study—examined the value of using a large multidimensional dataset collected from soldiers prior to deployment to Afghanistan for predicting post-deployment PTSD status. The dataset consisted of polygenic, epigenetic, metabolomic, endocrine, inflammatory and routine clinical lab markers, computerized neurocognitive testing, and symptom self-reports. The analysis was computed on active-duty Army personnel (N = 473) of the 101st Airborne at Fort Campbell, Kentucky. Machine-learning models predicted provisional PTSD diagnosis 90–180 days post deployment (random forest: AUC = 0.78, 95% CI = 0.67–0.89, sensitivity = 0.78, specificity = 0.71; SVM: AUC = 0.88, 95% CI = 0.78–0.98, sensitivity = 0.89, specificity = 0.79) and longitudinal PTSD symptom trajectories identified with latent growth mixture modeling (random forest: AUC = 0.85, 95% CI = 0.75–0.96, sensitivity = 0.88, specificity = 0.69; SVM: AUC = 0.87, 95% CI = 0.79–0.96, sensitivity = 0.80, specificity = 0.85). Among the highest-ranked predictive features were pre-deployment sleep quality, anxiety, depression, sustained attention, and cognitive flexibility. Blood-based biomarkers including metabolites, epigenomic, immune, inflammatory, and liver function markers complemented the most important predictors. The clinical prediction of post-deployment symptom trajectories and provisional PTSD diagnosis based on pre-deployment data achieved high discriminatory power. The predictive models may be used to determine deployment readiness and to determine novel pre-deployment interventions to mitigate the risk for deployment-related PTSD. |
format | Online Article Text |
id | pubmed-8589682 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-85896822021-11-23 Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors Schultebraucks, Katharina Qian, Meng Abu-Amara, Duna Dean, Kelsey Laska, Eugene Siegel, Carole Gautam, Aarti Guffanti, Guia Hammamieh, Rasha Misganaw, Burook Mellon, Synthia H. Wolkowitz, Owen M. Blessing, Esther M. Etkin, Amit Ressler, Kerry J. Doyle, Francis J. Jett, Marti Marmar, Charles R. Mol Psychiatry Article Active-duty Army personnel can be exposed to traumatic warzone events and are at increased risk for developing post-traumatic stress disorder (PTSD) compared with the general population. PTSD is associated with high individual and societal costs, but identification of predictive markers to determine deployment readiness and risk mitigation strategies is not well understood. This prospective longitudinal naturalistic cohort study—the Fort Campbell Cohort study—examined the value of using a large multidimensional dataset collected from soldiers prior to deployment to Afghanistan for predicting post-deployment PTSD status. The dataset consisted of polygenic, epigenetic, metabolomic, endocrine, inflammatory and routine clinical lab markers, computerized neurocognitive testing, and symptom self-reports. The analysis was computed on active-duty Army personnel (N = 473) of the 101st Airborne at Fort Campbell, Kentucky. Machine-learning models predicted provisional PTSD diagnosis 90–180 days post deployment (random forest: AUC = 0.78, 95% CI = 0.67–0.89, sensitivity = 0.78, specificity = 0.71; SVM: AUC = 0.88, 95% CI = 0.78–0.98, sensitivity = 0.89, specificity = 0.79) and longitudinal PTSD symptom trajectories identified with latent growth mixture modeling (random forest: AUC = 0.85, 95% CI = 0.75–0.96, sensitivity = 0.88, specificity = 0.69; SVM: AUC = 0.87, 95% CI = 0.79–0.96, sensitivity = 0.80, specificity = 0.85). Among the highest-ranked predictive features were pre-deployment sleep quality, anxiety, depression, sustained attention, and cognitive flexibility. Blood-based biomarkers including metabolites, epigenomic, immune, inflammatory, and liver function markers complemented the most important predictors. The clinical prediction of post-deployment symptom trajectories and provisional PTSD diagnosis based on pre-deployment data achieved high discriminatory power. The predictive models may be used to determine deployment readiness and to determine novel pre-deployment interventions to mitigate the risk for deployment-related PTSD. Nature Publishing Group UK 2020-06-02 2021 /pmc/articles/PMC8589682/ /pubmed/32488126 http://dx.doi.org/10.1038/s41380-020-0789-2 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Schultebraucks, Katharina Qian, Meng Abu-Amara, Duna Dean, Kelsey Laska, Eugene Siegel, Carole Gautam, Aarti Guffanti, Guia Hammamieh, Rasha Misganaw, Burook Mellon, Synthia H. Wolkowitz, Owen M. Blessing, Esther M. Etkin, Amit Ressler, Kerry J. Doyle, Francis J. Jett, Marti Marmar, Charles R. Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors |
title | Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors |
title_full | Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors |
title_fullStr | Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors |
title_full_unstemmed | Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors |
title_short | Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors |
title_sort | pre-deployment risk factors for ptsd in active-duty personnel deployed to afghanistan: a machine-learning approach for analyzing multivariate predictors |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8589682/ https://www.ncbi.nlm.nih.gov/pubmed/32488126 http://dx.doi.org/10.1038/s41380-020-0789-2 |
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