BACKGROUND: Six epidemic waves of human infection with avian influenza A (H7N9) virus have emerged in China with high mortality. However, study on quantitative relationship between clinical indices in ill persons and H7N9 outcome (fatal and non-fatal) is still unclear. A retrospective cohort study was conducted to collect laboratory-confirmed cases with H7N9 viral infection from 2013 to 2015 in 23 hospitals across 13 cities in Guangdong Province, China. METHODS: Multivariable logistic regression model and classification tree model analyses were used to detect the threshold of selected clinical indices and risk factors for H7N9 death. The receiver operating characteristic curve (ROC) and analyses were used to compare survival and death distributions and differences between indices. A total of 143 cases with 90 survivors and 53 deaths were investigated. RESULTS: Average age (Odds Ratio (OR) = 1.036, 95% Confidence Interval (CI) = 1.016-1.057), interval days between dates of onset and confirmation (OR = 1.078, 95% CI = 1.004-1.157), interval days between onset and oseltamivir treatment (OR = 5.923, 95% CI = 1.877-18.687), body temperature (BT) (OR = 3.612, 95% CI = 1.914-6.815), white blood cell count (WBC) (OR = 1.212, 95% CI = 1.092-1.346) were significantly associated with H7N9 death after adjusting for confounders. The chance of death from H7N9 infection was 80.0% if BT was over 38.1 °C, and chance of death is 67.4% if WBC count was higher than 9.5 (10/L). Only 27.1% of patients who began oseltamivir treatment less than 9.5 days after disease onset died, compared to 68.8% of those who started treatment more than 15.5 days after onset. CONCLUSIONS The intervals between date of onset and confirmation of diagnosis, between date of onset to oseltamivir treatment, age, BT and WBC are found to be the best predictors of H7N9 mortality.
Adult ; Aged ; China ; epidemiology ; Confidence Intervals ; Female ; Hospitalization ; statistics & numerical data ; Humans ; Influenza A Virus, H7N9 Subtype ; pathogenicity ; Influenza, Human ; epidemiology ; mortality ; virology ; Logistic Models ; Male ; Middle Aged ; ROC Curve ; Retrospective Studies ; Risk Factors ; Young Adult