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Lee N, Smith SW, Hui DSC, Ye M, Zelyas N, Chan PKS, Drews SJ, Zapernick L, Wong R, Labib M, Shokoples S, Eurich DT. Development of an Ordinal Scale Treatment Endpoint for Adults Hospitalized With Influenza. Clin Infect Dis 2020; 73:e4369-e4374. [PMID: 32827251 DOI: 10.1093/cid/ciaa777] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 06/11/2020] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND An obstacle in influenza therapeutics development is the lack of clinical endpoints, especially in hospitalized patients. A single time-point binary outcome measure is limited by patients' diverse clinical trajectories and low event rates. METHODS A 6-point ordinal scale with ascending clinical status severity (scoring: discharged; subacute care; acute care without/with respiratory failure; intensive care unit [ICU]; death) was proposed to study outcomes of adults hospitalized with influenza. Individual patient data from 2 active surveillance cohorts' datasets (2015/2016-2017/2018; Edmonton, Hong Kong) was used for evaluation. The impact of neuraminidase inhibitor (NAI) treatment on longitudinal ordinal outcome changes over 30 days was analyzed using mixed-effects ordinal logistic regression and group-based trajectory models. RESULTS Patient (n = 1226) baseline characteristics included age (mean 68.0 years), virus-type (A 78.1%, B 21.9%), respiratory failure (57.2%), ICU admittance (14.4%), and NAI treatment within 5 days of illness (69.2%). Outcomes at 30 days included discharged (75.2%), subacute care (13.7%), acute care (4.5%), and death (6.6%). Two main clinical trajectories were identified, predictive by baseline scoring (mean ± SD, 4.3 ± 0.6 vs 3.5 ± 0.6, P < .001). Improved outcomes with NAI treatment within 5 days were indicated by significantly lower clinical status scores over time (unadjusted odds ratio [OR], 0.53; 95% confidence interval [CI], .41-.69; P < .001; adjusted OR, 0.62; 95% CI, .50-.77; P < .001, for baseline score, age, and within-patient correlations). In subanalysis, influenza vaccination was also associated with lower scores (adjusted OR, 0.67; 95% CI, .50-.90; P = .007). Analyses of binary endpoints showed insignificant results. CONCLUSIONS The ordinal outcome scale is a potentially useful clinical endpoint for influenza therapeutic trials, which could account for the diverse clinical trajectories of hospitalized patients, warranting further development.
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Affiliation(s)
- Nelson Lee
- Division of Infectious Diseases, Department of Medicine, University of Alberta, Edmonton, Canada
| | - Stephanie W Smith
- Division of Infectious Diseases, Department of Medicine, University of Alberta, Edmonton, Canada
| | - David S C Hui
- Department of Medicine, Chinese University of Hong Kong, HKSAR, PRC.,Stanley Ho Centre for Emerging Infectious Diseases, Chinese University of Hong Kong, HKSAR, PRC
| | - Ming Ye
- School of Public Health, University of Alberta, Edmonton, Canada
| | - Nathan Zelyas
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Canada
| | - Paul K S Chan
- Stanley Ho Centre for Emerging Infectious Diseases, Chinese University of Hong Kong, HKSAR, PRC.,Department of Microbiology, Chinese University of Hong Kong, Hong Kong Special Administrative Region, People's Republic of China
| | - Steven J Drews
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Canada
| | - Lori Zapernick
- Division of Infectious Diseases, Department of Medicine, University of Alberta, Edmonton, Canada
| | - Rity Wong
- Department of Medicine, Chinese University of Hong Kong, HKSAR, PRC
| | - Mary Labib
- Division of Infectious Diseases, Department of Medicine, University of Alberta, Edmonton, Canada
| | | | - Dean T Eurich
- School of Public Health, University of Alberta, Edmonton, Canada
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Chen J, Zhu H, Horby PW, Wang Q, Zhou J, Jiang H, Liu L, Zhang T, Zhang Y, Chen X, Deng X, Nikolay B, Wang W, Cauchemez S, Guan Y, Uyeki TM, Yu H. Specificity, kinetics and longevity of antibody responses to avian influenza A(H7N9) virus infection in humans. J Infect 2020; 80:310-319. [PMID: 31954742 PMCID: PMC7112568 DOI: 10.1016/j.jinf.2019.11.024] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 10/26/2019] [Accepted: 11/08/2019] [Indexed: 11/29/2022]
Abstract
OBJECTIVES The long-term dynamics of antibody responses in patients with influenza A(H7N9) virus infection are not well understood. METHODS We conducted a longitudinal serological follow-up study in patients who were hospitalized with A(H7N9) virus infection, during 2013-2018. A(H7N9) virus-specific antibody responses were assessed by hemagglutination inhibition (HAI) and neutralization (NT) assays. A random intercept model was used to fit a curve to HAI antibody responses over time. HAI antibody responses were compared by clinical severity. RESULTS Of 67 patients with A(H7N9) virus infection, HAI antibody titers reached 40 on average 11 days after illness onset and peaked at a titer of 290 after three months, and average titers of ≥80 and ≥40 were present until 11 months and 22 months respectively. HAI antibody responses were significantly higher in patients who experienced severe disease, including respiratory failure and acute respiratory distress syndrome, compared with patients who experienced less severe illness. CONCLUSIONS Patients with A(H7N9) virus infection who survived severe disease mounted higher antibody responses that persisted for longer periods compared with those that experienced moderate disease. Studies of convalescent plasma treatment for A(H7N9) patients should consider collection of donor plasma from survivors of severe disease between 1 and 11 months after illness onset.
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Affiliation(s)
- Junbo Chen
- School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Building 8, 130 Dong'an Road, Xuhui District, Shanghai 200032, China
| | - Huachen Zhu
- Joint Institute of Virology (STU-HKU), Shantou University, Shantou 515041, China; State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong, Hong Kong, China
| | - Peter W Horby
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Qianli Wang
- School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Building 8, 130 Dong'an Road, Xuhui District, Shanghai 200032, China
| | - Jiaxin Zhou
- School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Building 8, 130 Dong'an Road, Xuhui District, Shanghai 200032, China
| | - Hui Jiang
- Beijing Chest Hospital, Capital Medical University, Beijing 101149, China; Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing 101149, China
| | - Liwei Liu
- Joint Institute of Virology (STU-HKU), Shantou University, Shantou 515041, China
| | - Tianchen Zhang
- Jiangxi Provincial Center for Disease Control and Prevention, Nanchang 330000, China
| | - Yongli Zhang
- Savaid Medical School, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xinhua Chen
- School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Building 8, 130 Dong'an Road, Xuhui District, Shanghai 200032, China
| | - Xiaowei Deng
- School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Building 8, 130 Dong'an Road, Xuhui District, Shanghai 200032, China
| | - Birgit Nikolay
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, 75015 Paris, France
| | - Wei Wang
- School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Building 8, 130 Dong'an Road, Xuhui District, Shanghai 200032, China
| | - Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, 75015 Paris, France
| | - Yi Guan
- Joint Institute of Virology (STU-HKU), Shantou University, Shantou 515041, China; State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong, Hong Kong, China
| | - Timothy M Uyeki
- Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Hongjie Yu
- School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Building 8, 130 Dong'an Road, Xuhui District, Shanghai 200032, China.
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Abstract
PURPOSE OF REVIEW Neuraminidase inhibitors (NAIs), including oseltamivir, zanamivir, and peramivir, is the main class of antiviral available for clinical use. As such, development of resistance toward these agents is of great clinical and public health concern. RECENT FINDINGS At present, NAI resistance remains uncommon among the circulating viruses (oseltamivir <3.5%, zanamivir <1%). Resistance risk is slightly higher in A(H1N1) than A(H3N2) and B viruses. Resistance may emerge during drug exposure, particularly among young children (<5 years), the immunocompromised, and individuals receiving prophylactic regimens. H275Y A(H1N1) variant, showing high-level oseltamivir resistance, is capable of causing outbreaks. R294K A(H7N9) variant shows reduced inhibition across NAIs. Multi-NAI resistance has been reported in the immunocompromised. SUMMARY These findings highlight the importance of continuous surveillance, and assessment of viral fitness and transmissibility of resistant virus strains. Detection can be challenging, especially in a mix of resistant and wild-type viruses. Recent advances in molecular techniques (e.g. targeted mutation PCR, iART, ddPCR, pyrosequencing, next-generation sequencing) have improved detection and our understanding of viral dynamics. Treatment options available for oseltamivir-resistant viruses are limited, and susceptibility testing of other NAIs may be required, but non-NAI antivirals (e.g. polymerase inhibitors) that are active against these resistant viruses are in late-stage clinical development.
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