A Framework for Inferring and Analyzing Pharmacotherapy Treatment Patterns

Abstract

AbstractObjectiveTo discover pharmacotherapy prescription patterns and their statistical associations with outcomes through a clinical pathway inference framework applied on real-world data.Materials and MethodsWe apply machine learning steps in our framework using a 2006 to 2020 cohort of veterans with major depressive disorder (MDD). Outpatient antidepressant pharmacy and emergency department visits, self-harm, and all-cause mortality data were extracted from the Department of Veterans Affairs Corporate Data Warehouse.ResultsOur MDD cohort consisted of 252,179 individuals. During the study period there were 98,417 cases of emergency department visits, 1,016 cases of self-harm, and 1,507 deaths from all causes. The top ten prescription patterns accounted for 69.3% of the data for individuals starting antidepressants at the fluoxetine equivalent of 20-39mg. Additionally, we found associations between outcomes and dosage change.DiscussionFor 252,179 Veterans who served in Iraq and Afghanistan with subsequent MDD noted in their electronic medical record, we documented and described the major pharmacotherapy prescription patterns implemented by VHA providers. Ten patterns accounted for almost 70% of the data. Associations between antidepressant usage and outcomes in observational data may be confounded. The low numbers of adverse events especially associated with all-cause mortality make our calculations imprecise. Furthermore, our outcomes are also the indications for both disease and treatment. Despite these limitations, we demonstrate the usefulness of our framework in providing operational insight into clinical practice, and our results underscore the need for increased monitoring during critical points of treatment.