Medication use evaluation of tocilizumab implementation in COVID-19 treatment guidelines: A causal inference approach

Abstract

Abstract Purpose Introduction of new medications to health-system formularies is often not accompanied by assessments of their clinical impact on the local patient population. The growing availability of electronic health record (EHR) data and advancements in pharmacoepidemiology methods offer institutions the opportunity to monitor the medication implementation process and assess clinical effectiveness in the local clinical context. In this study, we applied novel causal inference methods to evaluate the effects of a formulary policy introducing tocilizumab therapy for critically ill patients with coronavirus disease 2019 (COVID-19). Methods We conducted a medication use evaluation utilizing EHR data from patients admitted to a large medical center during the 6 months before and after implementation of a formulary policy endorsing the use of tocilizumab for treatment of COVID-19. The impact of tocilizumab on 28-day all-cause mortality was assessed using a difference-in-differences analysis, with ineligible patients serving as a nonequivalent control group, and a matched analysis guided by a target trial emulation framework. Safety endpoints assessed included the incidence of secondary infections and liver enzyme elevations. Our findings were benchmarked against clinical trials, an observational study, and a meta-analysis. Results Following guideline modification, tocilizumab was administered to 69% of eligible patients. This implementation was associated with a 3.1% absolute risk reduction in 28-day mortality (odds ratio, 0.86; number needed to treat to prevent one death, 32) attributable to the inclusion of tocilizumab in the guidelines and an additional 8.6% absolute risk reduction (odds ratio, 0.65; number needed to treat to prevent one death, 12) linked to its administration. These findings were consistent with estimates from published literature, although the effect estimates from the difference-in-differences analysis exhibited imprecision. Conclusion Evaluating formulary management decisions through novel causal inference approaches offers valuable estimates of clinical effectiveness and the potential to optimize the impact of new medications on population outcomes.