Effectiveness of oseltamivir treatment on clinical failure in hospitalized patients with lower respiratory tract infection
Timothy L Wiemken, Stephen P Furmanek, Ruth M Carrico, Paula Peyrani, Daniel Hoft, Alicia M Fry, Julio A Ramirez, Timothy L Wiemken, Stephen P Furmanek, Ruth M Carrico, Paula Peyrani, Daniel Hoft, Alicia M Fry, Julio A Ramirez
Abstract
Background: Influenza is associated with excess morbidity and mortality of individuals each year. Few therapies exist for treatment of influenza infection, and each require initiation as early as possible in the course of infection, making efficacy difficult to estimate in the hospitalized patient with lower respiratory tract infection. Using causal machine learning methods, we re-analyze data from a randomized trial of oseltamivir versus standard of care aimed at reducing clinical failure in hospitalized patients with lower respiratory tract infection during the influenza season.
Methods: This was a secondary analysis of the Rapid Empiric Treatment with Oseltamivir Study (RETOS). Conditional average treatment effects (CATE) and 95% confidence intervals were computed from causal forest including 85 clinical and demographic variables. RETOS was a multicenter, randomized, unblinded, trial of adult patients hospitalized with lower respiratory tract infections in Kentucky from 2009 through 2012. Adult hospitalized patients with lower respiratory tract infection were randomized to standard of care or standard of care plus oseltamivir as early as possible after hospital admission but within 24 h of enrollment. After randomization, oseltamivir was initiated in the treatment arm per package insert. The primary outcome was clinical failure, a composite measure including failure to reach clinical improvement within 7 days, transfer to intensive care 24 h after admission, or rehospitalization or death within 30 days.
Results: A total of 691 hospitalized patients with lower respiratory tract infections were included in the study. The only subgroup of patients with a statistically significant CATE was those with laboratory-confirmed influenza infection with a 26% lower risk of clinical failure when treated with oseltamivir (95% CI 3.2-48.0%).
Conclusions: 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 supportive of current recommendations to initiate antiviral treatment in hospitalized patients with confirmed or suspected influenza as soon as possible after admission. Trial registration Original trial: Clinical Trials.Gov; Rapid Empiric Treatment With Oseltamivir Study (RETOS) (RETOS); ClinicalTrials.gov Identifier: NCT01248715 https://ichgcp.net/clinical-trials-registry/NCT01248715.
Keywords: Causal forest; Flu; Heterogenous treatment effects; Tamiflu.
Conflict of interest statement
The authors declare no competing interests.
© 2021. The Author(s).
Figures
References
- Centers for Disease Control and Prevention. Disease burden of influenza. . Updated 5 Oct 2020. Accessed 15 Mar 2021.
- Jackson RJ, Cooper KL, Tappenden P, et al. Oseltamivir, zanamivir and amantadine in the prevention of influenza: a systematic review. J Infect. 2011;62(1):14–25. doi: 10.1016/j.jinf.2010.10.003.
- Uyeki TM, Bernstein HH, Bradley JS, et al. Clinical Practice Guidelines by the Infectious Diseases Society of America: 2018 update on diagnosis, treatment, chemoprophylaxis, and institutional outbreak management of seasonal influenzaa. Clin Infect Dis. 2019;68(6):895–902. doi: 10.1093/cid/ciy874.
- Ramirez J, Peyrani P, Wiemken T, Chaves SS, Fry AM. A randomized study evaluating the effectiveness of oseltamivir initiated at the time of hospital admission in adults hospitalized with influenza-associated lower respiratory tract infections. Clin Infect Dis. 2018;67(5):736–742. doi: 10.1093/cid/ciy163.
- Wiemken TL, Kelley RR. Machine learning in epidemiology and health outcomes research. Annu Rev Public Health. 2020;41:21–36. doi: 10.1146/annurev-publhealth-040119-094437.
- Baum A, Scarpa J, Bruzelius E, Tamler R, Basu S, Faghmous J. Targeting weight loss interventions to reduce cardiovascular complications of type 2 diabetes: a machine learning-based post-hoc analysis of heterogeneous treatment effects in the Look AHEAD trial. Lancet Diabetes Endocrinol. 2017;5(10):808–815. doi: 10.1016/S2213-8587(17)30176-6.
- Athey S, Tibshirani J, Wager S. Generalized random forests. Ann Stat. 2019;47(2):1148–1178. doi: 10.1214/18-AOS1709.
- Wager S, Athey S. Estimation and inference of heterogeneous treatment effects using random forests. [math, stat]. 2015.
- Künzel SR, Sekhon JS, Bickel PJ, Yu B. Metalearners for estimating heterogeneous treatment effects using machine learning. Proc Natl Acad Sci. 2019;116(10):4156–4165. doi: 10.1073/pnas.1804597116.
- Generalized Random Forests [computer program]. Version 1.2.0. 2020.
- Marafino BJ, Schuler A, Liu VX, Escobar GJ, Baiocchi M. Predicting preventable hospital readmissions with causal machine learning. Health Serv Res. 2020;55(6):993–1002. doi: 10.1111/1475-6773.13586.
- Elek P, Biro A. Regional differences in diabetes across Europe—regression and causal forest analyses. Econ Hum Biol. 2021;40:100948. doi: 10.1016/j.ehb.2020.100948.
- Wang L, Cao F, Wang S, Sun M, Dong L. Using k-dependence causal forest to mine the most significant dependency relationships among clinical variables for thyroid disease diagnosis. PLoS ONE. 2017;12(8):e0182070. doi: 10.1371/journal.pone.0182070.
- Scarpa J, Bruzelius E, Doupe P, Le M, Faghmous J, Baum A. Assessment of risk of harm associated with intensive blood pressure management among patients with hypertension who smoke: a secondary analysis of the systolic blood pressure intervention trial. JAMA Netw Open. 2019;2(3):e190005. doi: 10.1001/jamanetworkopen.2019.0005.
Source: PubMed