Literature DB >> 31622983

A Risk Classification Model to Predict Mortality Among Laboratory-Confirmed Avian Influenza A H7N9 Patients: A Population-Based Observational Cohort Study.

Leonardo Martinez1,2, Wei Cheng3, Xiaoxiao Wang3, Feng Ling3, Lan Mu4, Changwei Li1, Xiang Huo5, Mark H Ebell1, Haodi Huang5, Limei Zhu5, Chao Li1, Enfu Chen3, Andreas Handel1, Ye Shen1.   

Abstract

BACKGROUND: Avian influenza A H7N9 (A/H7N9) is characterized by rapid progressive pneumonia and respiratory failure. Mortality among laboratory-confirmed cases is above 30%; however, the clinical course of disease is variable and patients at high risk for death are not well characterized.
METHODS: We obtained demographic, clinical, and laboratory information on all A/H7N9 patients in Zhejiang province from China Centers for Disease Control and Prevention electronic databases. Risk factors for death were identified using logistic regression and a risk score was created using regression coefficients from multivariable models. We externally validated this score in an independent cohort from Jiangsu province.
RESULTS: Among 305 A/H7N9 patients, 115 (37.7%) died. Four independent predictors of death were identified: older age, diabetes, bilateral lung infection, and neutrophil percentage. We constructed a score with 0-13 points. Mortality rates in low- (0-3), medium- (4-6), and high-risk (7-13) groups were 4.6%, 32.1%, and 62.7% (Ptrend < .0001). In a validation cohort of 111 A/H7N9 patients, 61 (55%) died. Mortality rates in low-, medium-, and high-risk groups were 35.5%, 55.8, and 67.4% (Ptrend = .0063).
CONCLUSIONS: We developed and validated a simple-to-use, predictive risk score for clinical use, identifying patients at high mortality risk.
© The Author(s) 2019. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  H7N9 infection; influenza; mortality; risk score

Year:  2019        PMID: 31622983     DOI: 10.1093/infdis/jiz328

Source DB:  PubMed          Journal:  J Infect Dis        ISSN: 0022-1899            Impact factor:   5.226


  7 in total

1.  The role of telemedicine during the COVID-19 epidemic in China-experience from Shandong province.

Authors:  Xuan Song; Xinyan Liu; Chunting Wang
Journal:  Crit Care       Date:  2020-04-28       Impact factor: 9.097

2.  Novel coronavirus infection during the 2019-2020 epidemic: preparing intensive care units-the experience in Sichuan Province, China.

Authors:  Xuelian Liao; Bo Wang; Yan Kang
Journal:  Intensive Care Med       Date:  2020-02-05       Impact factor: 17.440

3.  A dynamic model for individualized prognosis prediction in patients with avian influenza A H7N9.

Authors:  Mingzhi Zhang; Ke Xu; Qigang Dai; Dongfang You; Zhaolei Yu; Changjun Bao; Yang Zhao
Journal:  Ann Transl Med       Date:  2022-02

4.  Predict Score: A New Biological and Clinical Tool to Help Predict Risk of Intensive Care Transfer for COVID-19 Patients.

Authors:  Mickael Gette; Sara Fernandes; Marion Marlinge; Marine Duranjou; Wijayanto Adi; Maelle Dambo; Pierre Simeone; Pierre Michelet; Nicolas Bruder; Regis Guieu; Julien Fromonot
Journal:  Biomedicines       Date:  2021-05-18

Review 5.  The management of coronavirus disease 2019 (COVID-19).

Authors:  Jialin Liu; Siru Liu
Journal:  J Med Virol       Date:  2020-05-22       Impact factor: 20.693

6.  SARS-CoV-2, other respiratory viruses and bacteria in aerosols: Report from Kuwait's hospitals.

Authors:  N Habibi; S Uddin; F Al-Salameen; S Al-Amad; V Kumar; M Al-Otaibi; N Abdul Razzack; A Shajan; F Shirshikar
Journal:  Indoor Air       Date:  2021-06-14       Impact factor: 6.554

7.  Modified National Early Warning Score as Early Predictor of Outcome in COVID-19 Pandemic.

Authors:  Fabio Tagliabue; Daniele Schena; Luca Galassi; Matteo Magni; Guglielmo Guerrazzi; Andrea Acerbis; Christina Rinallo; Daniel Longhi; Alberto Ronzani; Pierpaolo Mariani
Journal:  SN Compr Clin Med       Date:  2021-06-18
  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.