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(Unpublished)
Abstract
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 |
| Creators/Authors: |
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| ETD Committee: |
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| 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 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/40160 |
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