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- Title
A Risk Classification Model to Predict Mortality Among Laboratory-Confirmed Avian Influenza A H7N9 Patients: A Population-Based Observational Cohort Study.
- Authors
Martinez, Leonardo; Cheng, Wei; Wang, Xiaoxiao; Ling, Feng; Mu, Lan; Li, Changwei; Huo, Xiang; Ebell, Mark H; Huang, Haodi; Zhu, Limei; Li, Chao; Chen, Enfu; Handel, Andreas; Shen, Ye
- Abstract
<bold>Background: </bold>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.<bold>Methods: </bold>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.<bold>Results: </bold>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).<bold>Conclusions: </bold>We developed and validated a simple-to-use, predictive risk score for clinical use, identifying patients at high mortality risk.
- Subjects
ZHEJIANG Sheng (China); JIANGSU Sheng (China); LUNG infections; MORTALITY; COHORT analysis; RESPIRATORY insufficiency; SCIENTIFIC observation; H7N9 Influenza
- Publication
Journal of Infectious Diseases, 2019, Vol 220, Issue 11, p1780
- ISSN
0022-1899
- Publication type
journal article
- DOI
10.1093/infdis/jiz328