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Nomogram for the Individualized Prediction of Survival Among Patients with H7N9 Infection

Authors Cheng Q, Sun Z, Zhao G, Xie L

Received 12 December 2019

Accepted for publication 6 March 2020

Published 20 March 2020 Volume 2020:13 Pages 255—269


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Professor Marco Carotenuto

Qinglin Cheng,1,2,* Zhou Sun,1,* Gang Zhao,1,* Li Xie1,*

1Division of Infectious Diseases, Hangzhou Center for Disease Control and Prevention, Hangzhou 310021, People’s Republic of China; 2School of Public Health, Zhejiang Chinese Medical University, Hangzhou 310021, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Qinglin Cheng; Zhou Sun
Division of Infectious Diseases, Hangzhou Center for Disease Control and Prevention, 568 Mingshi Road, Hangzhou 310021, People’s Republic of China

Background: Until recently, almost all of these studies have identified multiple risk factors but did not offer practical instruments for routine use in predicting individualized survival in human H7N9 infection cases. The objective of this study is to create a practical instrument for use in predicting an individualized survival probability of H7N9 patients.
Methods: A matched case–control study (1:2 ratios) was performed in Zhejiang Province between 2013 and 2019. We reviewed specific factors and outcomes regarding patients with H7N9 virus infection (VI) to determine relationships and developed a nomogram to calculate individualized survival probability. This tool was used to predict each individual patient’s probability of survival based on results obtained from the multivariable Cox proportional hazard regression analysis.
Results: We examined 227 patients with H7N9 VI enrolled in our study. Stepwise selection was applied to the data, which resulted in a final model with 8 independent predictors [including initial PaO2/FiO2 ratio ≤ 300 mmHg, age ≥ 60 years, chronic diseases, poor hand hygiene, time from illness onset to the first medical visit, incubation period ≤ 5 days, peak C-reactive protein ≥ 120 mg/L], and initial bilateral lung infection. The concordance index of this nomogram was 0.802 [95% confidence interval (CI): 0.694– 0.901] and 0.793 (95% CI: 0.611– 0.952) for the training and validation sets, respectively, which indicates adequate discriminatory power. The calibration curves for the survival showed optimal agreement between nomogram prediction and actual observation in the training and validation sets, respectively.
Conclusion: We established and validated a novel nomogram that can accurately predict the survival probability of patients with H7N9 VI. This nomogram can serve an important role in counseling patients with H7N9 VI and guide treatment decisions.

Keywords: avian influenza virus, H7N9, survival, predictors, nomogram

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