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.