PROJECT SUMMARY The objective of this NIDCR Mentored Career Development Award to Promote Diversity (K01) is to provide Dr. Juan Hincapie-Castillo with the essential training to become an independent researcher in real-world evidence for precision pain management applied to trigeminal neuralgia (TN). TN is associated with severe and debilitating orofacial pain that can result in a significant decline in the quality of life for older adults. The aging population experiencing TN is at high risk for central nervous system depression with psychotropic medications, including antiepileptic drugs. Gabapentin, an antiepileptic commonly used to treat neuropathic pain, is often prescribed as the initial medication for TN despite clinical recommendations of using carbamazepine or oxcarbazepine as first line treatments – to date no evidence exists, outside limited case reports, on the patient-reported lived experience nor comparative safety of these drugs in the real world. Although real-world data sources, such as Medicare administrative claims, offer an opportunity to evaluate drug-related health outcomes, valid causal inference requires contextual clinical knowledge and competency in advanced analytical methods. The training goals in this K01 include: (1) Expanding clinical expertise in the substantive area of orofacial pain and TN; (2) Building skills in primary data collection and engagement of people with lived experience of pain in research; and (3) Learning and applying robust advanced causal inference methods and machine learning algorithms for assessment of treatment effect heterogeneity. This career development award will be carried out at the University of North Carolina at Chapel Hill and will be guided by a mentoring team comprising leading experts in biomedical research. Leveraging this mentored training, Dr. Hincapie-Castillo will apply new expertise in TN and analytical skills into the following research aims: (1) Document patient-reported experiences of initial diagnosis and treatment of TN through the collection and synthesis of primary qualitative and quantitative data; (2) Develop and validate a Medicare claims-based algorithm for incident diagnoses of TN by appraising reference diagnostic data from medical charts from a large academic hospital; and (3) Investigate the risk of injurious falls caused by antiepileptics among Medicare- insured patients diagnosed with TN using advanced causal inference pharmacoepidemiologic methods and causal forests to identify variation in treatment effects. Study findings will generate critical real-world evidence to guide patient-centered care in TN by contextualizing and assessing drug safety concerns relevant to older adults. Extending beyond comparative safety evaluations in this patient population, this K01 award will lay the foundation for future R01-level research focused on identifying effective pharmacological and non- pharmacological TN treatments using novel machine learning methods in real-worl...