Effectiveness of neuraminidase inhibitors to prevent mortality in patients with laboratory-confirmed avian influenza A H7N9.


Journal

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases
ISSN: 1878-3511
Titre abrégé: Int J Infect Dis
Pays: Canada
ID NLM: 9610933

Informations de publication

Date de publication:
Feb 2021
Historique:
received: 07 10 2020
revised: 09 12 2020
accepted: 10 12 2020
pubmed: 18 12 2020
medline: 5 5 2021
entrez: 17 12 2020
Statut: ppublish

Résumé

Avian influenza virus A(H7N9) remains a threat to humans and has great potential to cause a pandemic in the foreseeable future. Antiviral treatment with neuraminidase inhibitors has been recommended to treat patients with H7N9 infection as early as possible, although evidence-based research on their effectiveness for H7N9 infection is lacking. Data from all laboratory-confirmed cases of H7N9 infection in Zhejiang Province between 2013 and 2017 were retrieved, and time-dependent survival models were used to evaluate the effectiveness of treatment with neuraminidase inhibitors to reduce the risk of mortality. The final optimal model found no significant association (odds ratio 1.29, 95% confidence interval 0.78-2.15) between time to treatment with neuraminidase inhibitors and survival after controlling for age and white blood cell count. Sensitivity analyses with multiple imputation for missing data concurred with the primary analysis. No association was found between treatment with neuraminidase inhibitors and survival in patients with H7N9 infection using various adjusted models and sensitivity analyses of missing data imputations.

Identifiants

pubmed: 33333253
pii: S1201-9712(20)32551-0
doi: 10.1016/j.ijid.2020.12.028
pii:
doi:

Substances chimiques

Antiviral Agents 0
Neuraminidase EC 3.2.1.18

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

573-578

Informations de copyright

Published by Elsevier Ltd.

Auteurs

Wei Cheng (W)

Zhejiang Provincial Centre for Disease Control and Prevention, Hangzhou, China.

Anqi Pan (A)

University of Georgia, College of Public Health, Department of Epidemiology and Biostatistics, Athens, GA, USA.

Stephen L Rathbun (SL)

University of Georgia, College of Public Health, Department of Epidemiology and Biostatistics, Athens, GA, USA.

Yang Ge (Y)

University of Georgia, College of Public Health, Department of Epidemiology and Biostatistics, Athens, GA, USA.

Qian Xiao (Q)

University of Georgia, Department of Statistics, Athens, GA, USA.

Leonardo Martinez (L)

Stanford University, School of Medicine, Division of Infectious Diseases and Geographic Medicine, Stanford, CA, USA.

Feng Ling (F)

Zhejiang Provincial Centre for Disease Control and Prevention, Hangzhou, China.

Shelan Liu (S)

Zhejiang Provincial Centre for Disease Control and Prevention, Hangzhou, China.

Xiaoxiao Wang (X)

Zhejiang Provincial Centre for Disease Control and Prevention, Hangzhou, China.

Zhao Yu (Z)

Zhejiang Provincial Centre for Disease Control and Prevention, Hangzhou, China.

Mark H Ebell (MH)

University of Georgia, College of Public Health, Department of Epidemiology and Biostatistics, Athens, GA, USA.

Changwei Li (C)

University of Georgia, College of Public Health, Department of Epidemiology and Biostatistics, Athens, GA, USA; Tulane University School of Public Health and Tropical Medicine, Department of Epidemiology, New Orleans, LA, USA.

Andreas Handel (A)

University of Georgia, College of Public Health, Department of Epidemiology and Biostatistics, Athens, GA, USA; University of Georgia, College of Public Health, Health Informatics Institute, Athens, GA, USA; University of Georgia, Center for the Ecology of Infectious Diseases, Athens, GA, USA.

Enfu Chen (E)

Zhejiang Provincial Centre for Disease Control and Prevention, Hangzhou, China.

Ye Shen (Y)

University of Georgia, College of Public Health, Department of Epidemiology and Biostatistics, Athens, GA, USA. Electronic address: yeshen@uga.edu.

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Classifications MeSH