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Association of early chronic systemic inflammation with depression at 12 months post-traumatic brain injury and a comparison of prediction models

Awan, Nabil (2021) Association of early chronic systemic inflammation with depression at 12 months post-traumatic brain injury and a comparison of prediction models. Master's Thesis, University of Pittsburgh. (Unpublished)

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Background: Post-traumatic depression (PTD) is a common condition after traumatic brain injury (TBI), which is believed to be potentiated by systemic inflammation. The objective of this study was to study the role of early chronic (1-3 months post-TBI) systemic neuroinflammation on 12 months PTD following moderate-to-severe TBI and build prediction models.
Methods: Data from participants (n=149) recruited from inpatient rehabilitation centers at the University of Pittsburgh Medical Center (UPMC) was used. Distributions 33 different neuroinflammatory markers, derived from blood samples collected 1-3 months post-injury, were graphed. Descriptive statistics for selected covariates (age, sex, injury severity, 1-6 months antidepressant use history, premorbid depression) were summarized using mean, median, interquartile range (IQR), standard deviations (SD), and percentages (%). Simple logistic regressions were used to identify several biomarkers associated with PTD (p-value <0.10). Principal components analysis (PCA) and ridge regression were then employed to create an overall inflammatory load score (ILS). PTD prediction model performance was compared using a logistic regression and a random forest modeling and their variations (up-sampling) using both internal and external validations.
Results: 1-3 months MIP-1α, RANTES, ITAC, MIP-3α, IL-1b, TNFα, sIL-6R, IL-21, GM-CSF, MIP-1b, IL-7, IL-10, and Fractalkine were associated (p-value < 0.10) with 12 months PTD in the univariate logistic regressions. The ridge regression-based ILS outperformed the first three PCA-based ILS [area under the curve, AUC=84.52% (ridge) vs. 83.62% (3-PCA) and 81.62% (1-PCA)]. An internal validation approach using 100 bootstrapped datasets identified random forest model with up-sampling procedure as the best performing model (92.4% average accuracy, 69.9% average sensitivity, and 96.2% average specificity). PTD significantly mediated the ILS-functional outcomes relationships.
Conclusion: Early chronic systemic inflammation specific to different areas of immune function can help predict PTD with considerable accuracy. A random forest model with an up-sampling procedure performed better than logistic regression in all prediction metrics using a robust internal (bootstrapping) validation.
Public health significance: Depression is treatable, and biomarkers associated with depression have utility as a screening tool for PTD prevention and early treatment, minimizing negative consequences like suicidality. It may have additional benefits for daily functioning, including cognition, behavior, and community reintegration.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Awan, Nabilnaa86@pitt.edunaa860000-0001-6396-9095
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairWahed, Abdus Swahed@pitt.eduwahed
Committee CoChairBuchanich, Jeanine Mjeanine@pitt.edujeanine
Committee MemberYouk, Ada Oayouk@pitt.eduayouk
Committee MemberCarlson, Jennajnc35@pitt.edujnc35
Committee MemberWagner, Amy Kwagnerak@upmc.eduakw4
Date: 19 January 2021
Date Type: Publication
Defense Date: 17 August 2020
Approval Date: 19 January 2021
Submission Date: 12 January 2021
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 72
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Biostatistics
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: depression, neuroinflammation, traumatic brain injury
Date Deposited: 19 Jan 2021 20:42
Last Modified: 19 Jan 2021 20:42


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