Discussion:
This study suggests that addition of oseltamivir to standard of care may
decrease clinical failure in hospitalized patients with
influenza-associated lower respiratory tract infection versus standard
of care alone. These results are reasonably consistent with the
reductions in clinical failure with oseltamivir treatment in the
per-protocol analysis from our original randomized trial which
identified a non-significant reduction from 24% in the standard of care
arm to 14% in the standard of care plus oseltamivir arm (P=0.414) (4).
Given the underpowered nature of the original study, using novel methods
such as causal forests may allow for alternative estimation of treatment
effects among subgroups versus traditional biostatistical analyses.
Here, the presence of influenza was the primary driver of significant
decreases in clinical failure with oseltamivir therapy. This suggests
that our approach to estimate conditional average treatment effects
through causal machine learning methods is likely accurate and
potentially more useful for detection of subgroup treatment effects in
small samples. Future randomized trials may benefit from using similar
methodologies as an adjunctive measure for the more traditional
frequentist statistical methodologies typically utilized and reported.
Use of these novel methods may assist in detection of subgroups where
interventions are beneficial or detrimental, allowing for a movement
toward more personalized medicine.
This study has several limitations. First, given the small sample size
in the study, the variability in our treatment effect estimates is wide,
as indicated by many of the 95% confidence intervals for many
variables. Further, we were not able to assess model performance through
training and testing given the small sample size, resulting in
potentially biased results. Second, the generalizability of treatment
effect estimates from causal forest methodologies has yet to be widely
documented. These machine learning models for computation of
heterogenous treatment effects have only begun to be utilized in
medicine (6, 11, 12) and we were unable to find any studies using these
methods in the field of respiratory infections.
The strengths of this study include both the data used from the largest
randomized trial of hospitalized patients with oseltamivir therapy and
the consistency of results from the initial trial using traditional
methodologies such as regression modeling.
Future studies may benefit from these methods as adjunctive analytics in
randomized trials and potentially for observational designs where
appropriate adjustments can be made with collected data. By continuing
to perform both methods, we can begin to identify the best analytic
approaches to identify more targeted treatments to improve patient
outcomes.
In conclusion, this secondary analysis of a randomized clinical trial
suggests that oseltamivir may have clinical utility in hospitalized
patients with influenza-associated lower respiratory tract infections.
These results are supportive of current recommendations to initiate
antiviral treatment in hospitalized patients with confirmed or suspected
influenza as soon as possible after admission (3).
Disclaimer. The findings and conclusions in this report
are those of the authors and do not necessarily represent the views of
the CDC, US Department of Health and Human Services.
Financial support. This study was not funded. However,
the original randomized trial was supported by an award to the
University of Louisville by the CDC (cooperative agreement IP000420-01).
Potential conflicts of interest. All authors: No
reported conflicts of interest.