Introduction:
Influenza is associated with excess morbidity and mortality of individuals each year in the United States and contributes substantially to the national healthcare burden each winter (1). Neuraminidase inhibitors such as oseltamivir, peramivir, and zanamivir are one of three categories of FDA-approved therapies for influenza illness and reduce duration of infection through prevention of virus exit from infected cells (2). Because of this mechanism, administration of drug early in the course of infection is most efficacious (3). We previously conducted a randomized trial to evaluate the impact of oseltamivir on clinical outcomes of hospitalized patients with lower respiratory tract infection associated with influenza (4). In that study, we found limited, and not statistically significant, efficacy of oseltamivir in reducing clinical failure, a composite measure including failure to reach clinical improvement within 7 days, transfer to intensive care 24 hours after admission, or rehospitalization or death within 30 days, in hospitalized patients with influenza-associated lower respiratory tract infection (4). Although the average treatment effect was not significant, it is possible that therapy had clinical benefits in subgroups of patients, or our analytical approach was insufficient for the data obtained.
Since the results of this study were published, there have been many innovations in analytical approaches for these types of data, specifically the field of machine learning (5). These advancements have improved not only our ability to develop predictive models but also allow for computation of treatment effects. In this area, treatment effect computation is also possible across subgroups of individuals, with fewer limitations of sample size and false discovery rates that plague frequentist statistical approaches (6). Since the sample size of influenza virus infected patients in our initial randomized trial was relatively small and we were underpowered for our primary endpoint, it is possible that we were unable to appropriately detect subgroups in which oseltamivir therapy was efficacious.
The objective of this post hoc study was to utilize a novel machine learning method, the causal forest (7), to evaluate subgroups of hospitalized patients with lower respiratory tract infection who may have differential therapeutic response to oseltamivir therapy for prevention of clinical failure.