Abstract

Project Summary/Abstract Background: The use of central nervous system (CNS) stimulants such as amphetamine, dextroamphetamine, methylphenidate, armodafinil and modafinil is discouraged in patients with traumatic brain injury (TBI) as they have no proven benefits and carries the FDA black-box warning of a higher risk for developing substance use disorders (SUD). Significance: TBI is a major source of morbidity and mortality for Veterans, and a top HA/ORD/HSR&D priority. Our preliminary data suggest that as of May 2021, nationwide 728,065 Veterans had a diagnosis of TBI in their EHR. The TBI registry estimates that 81% of the Veterans have mild TBI. Veterans with TBI are more likely to receive CNS stimulants than those without TBI. Our preliminary data suggests that 5.8% (42,437/728,065) of the Veterans with TBI received prescriptions for CNS stimulants, which is over 10 times higher than that in Veterans without TBI (0.56%). Findings of our preliminary study also suggest that compared to non-users of CNS stimulants, users have a higher risk of SUD. Currently, there is no evidence-based therapy for treatment of mild TBI and the VA mild TBI guidelines discourages the use of medications to ameliorate neurocognitive symptoms. However, many Veterans with TBI receive prescriptions for CNS stimulants but less is known about the safety of these drugs in Veterans with TBI. Innovation & Impact: To the best of our knowledge, the study questions have never been answered before. The key innovation of the proposed study is in the filling of the scientific knowledge gap, the potential clinical implications of the findings, and the relevance to the Veteran population. Our methodological innovation will include the use of deep machine learning approaches including the impact and the interaction scores developed by our team to quantify the results of deep learning. Specific Aims: 1) To characterize stimulant prescription pattern in Veterans with mild TBI; 2) To test the hypothesis that initiation of stimulant therapy is associated with a higher risk of incident SUD, hospitalization, and mortality in Veterans with mild TBI; and 3) To develop an explainable machine (deep) learning risk prediction model that will allow a more accurate and precise assessment of clinical benefits vs. risk of stimulants in individual Veterans. Methodology: These aims will be achieved by using the VA TBI registry and EHR data. For Aims 1 and 3, we will use all Veterans with a TBI diagnosis and any use of stimulants. For Aim 2, we will emulate the design of an RCT, using Veterans with TBI free of prevalent SUD and new prescriptions of CNS stimulants after mild TBI diagnosis. We will then conduct sensitivity analysis in the subset of Veterans with mild TBI using the Comprehensive TBI Evaluation (CTBIE) tables. Propensity score matching will be used for outcome-blinded assembly of cohorts balanced on measured covariates, and sensitivity analyses will be used to estimate impact of unmeasured confounders. Centers for Medicare & Medicaid Services (CMS) data will be used to validate the generalizability of our prediction model. Next Steps/Implementation: In additions to the traditional dissemination approaches through presentations and publications, we will share our software tools with the VA AI center for dissemination and work with our operational partners to incorporate the findings of the proposed project into clinician education materials.

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DRA: Substance Use Disorders, Brain and Spinal Cord Injuries and Disorders

DRE: TRL - Applied/Translational, Data Science

Keywords: Addictive Disorders, TBI

MeSH Terms: None at this time.