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

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Title: Derivation and validation of risk prediction for posttraumatic stress symptoms following trauma exposure.
Authors: Kim, Raphael, Lin, Tina, Pang, Gehao, Liu, Yufeng, Tungate, Andrew S., Hendry, Phyllis L., Kurz, Michael C., Peak, David A., Jones, Jeffrey, Rathlev, Niels K., Swor, Robert A., Domeier, Robert, Velilla, Marc-Anthony, Lewandowski, Christopher, Datner, Elizabeth, Pearson, Claire, Lee, David, Mitchell, Patricia M., McLean, Samuel A., Linnstaedt, Sarah D.
Source: Psychological Medicine. Aug2023, Vol. 53 Issue 11, p4952-4961. 10p.
Subjects: Post-traumatic stress disorder, Risk assessment, Questionnaires, Prediction models, Longitudinal method
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. [ABSTRACT FROM AUTHOR]
Copyright of Psychological Medicine is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Derivation and validation of risk prediction for posttraumatic stress symptoms following trauma exposure.
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  Data: <searchLink fieldCode="AR" term="%22Kim%2C+Raphael%22">Kim, Raphael</searchLink><br /><searchLink fieldCode="AR" term="%22Lin%2C+Tina%22">Lin, Tina</searchLink><br /><searchLink fieldCode="AR" term="%22Pang%2C+Gehao%22">Pang, Gehao</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Yufeng%22">Liu, Yufeng</searchLink><br /><searchLink fieldCode="AR" term="%22Tungate%2C+Andrew+S%2E%22">Tungate, Andrew S.</searchLink><br /><searchLink fieldCode="AR" term="%22Hendry%2C+Phyllis+L%2E%22">Hendry, Phyllis L.</searchLink><br /><searchLink fieldCode="AR" term="%22Kurz%2C+Michael+C%2E%22">Kurz, Michael C.</searchLink><br /><searchLink fieldCode="AR" term="%22Peak%2C+David+A%2E%22">Peak, David A.</searchLink><br /><searchLink fieldCode="AR" term="%22Jones%2C+Jeffrey%22">Jones, Jeffrey</searchLink><br /><searchLink fieldCode="AR" term="%22Rathlev%2C+Niels+K%2E%22">Rathlev, Niels K.</searchLink><br /><searchLink fieldCode="AR" term="%22Swor%2C+Robert+A%2E%22">Swor, Robert A.</searchLink><br /><searchLink fieldCode="AR" term="%22Domeier%2C+Robert%22">Domeier, Robert</searchLink><br /><searchLink fieldCode="AR" term="%22Velilla%2C+Marc-Anthony%22">Velilla, Marc-Anthony</searchLink><br /><searchLink fieldCode="AR" term="%22Lewandowski%2C+Christopher%22">Lewandowski, Christopher</searchLink><br /><searchLink fieldCode="AR" term="%22Datner%2C+Elizabeth%22">Datner, Elizabeth</searchLink><br /><searchLink fieldCode="AR" term="%22Pearson%2C+Claire%22">Pearson, Claire</searchLink><br /><searchLink fieldCode="AR" term="%22Lee%2C+David%22">Lee, David</searchLink><br /><searchLink fieldCode="AR" term="%22Mitchell%2C+Patricia+M%2E%22">Mitchell, Patricia M.</searchLink><br /><searchLink fieldCode="AR" term="%22McLean%2C+Samuel+A%2E%22">McLean, Samuel A.</searchLink><br /><searchLink fieldCode="AR" term="%22Linnstaedt%2C+Sarah+D%2E%22">Linnstaedt, Sarah D.</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Psychological+Medicine%22">Psychological Medicine</searchLink>. Aug2023, Vol. 53 Issue 11, p4952-4961. 10p.
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Psychological Medicine is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1017/S003329172200191X
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