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.