Abstract
BackgroundInfleunza is a challenging issue in public health. The mortality and morbidity associated with epidemic and pandemic influenza puts a heavy burden on health care system. Most patients with influenza can be treated on an outpatient basis but some required critical care. It is crucial for frontline physicians to stratify influenza patients by level of risk. Therefore, this study aimed to create a prediction model for critical care and in-hospital mortality.MethodsThis retrospective cohort study extracted data from the Chang Gung Research Database. This study included the patients who were diagnosed with influenza between 2010 and 2016. The primary outcome of this study was critical illness. The secondary analysis was to predict in-hospital mortality. A two-stage-modeling method was developed to predict hospital mortality. We constructed a multiple logistic regression model to predict the outcome of critical illness in the first stage, then S1 score were calculated. In the second stage, we used the S1 score and other data to construct a backward multiple logistic regression model. The area under the receiver operating curve was used to assess the predictive value of the model.ResultsIn the present study, 1680 patients met the inclusion criteria. The overall ICU admission and in-hospital mortality was 10.36% (174 patients) and 4.29% (72 patients), respectively. In stage I analysis, hypothermia (OR = 1.92), tachypnea (OR = 4.94), lower systolic blood pressure (OR = 2.35), diabetes mellitus (OR = 1.87), leukocytosis (OR = 2.22), leukopenia (OR = 2.70), and a high percentage of segmented neutrophils (OR = 2.10) were associated with ICU admission. Bandemia had the highest odds ratio in the Stage I model (OR = 5.43). In stage II analysis, C-reactive protein (OR = 1.01), blood urea nitrogen (OR = 1.02) and stage I model’s S1 score were assocaited with in-hospital mortality. The area under the curve for the stage I and II model was 0.889 and 0.766, respectively.ConclusionsThe two-stage model is a efficient risk-stratification tool for predicting critical illness and mortailty. The model may be an optional tool other than qSOFA and SIRS criteria.
Highlights
Infleunza is a challenging issue in public health
A modeling study investigating the number of influenza-associated respiratory deaths that occurred globally between 1999 and 2015 estimated that 291,243–645,832 respiratory deaths associated with seasonal influenza occurred each year (4.0–8.8 per 100,000 individuals)
In a 2009 pandemic caused by H1N1pdm09, the World Health Organization reported a global total of 18,449 laboratory-confirmed deaths by 1 August 2010 [3]
Summary
The mortality and morbidity associated with epidemic and pandemic influenza puts a heavy burden on health care system. Influenza has a long history worldwide, but remains a challenging issue in public health. In a 2009 pandemic caused by H1N1pdm, the World Health Organization reported a global total of 18,449 laboratory-confirmed deaths by 1 August 2010 [3]. A modeling study implied that the true burden of mortality from H1N1pdm during the 2009 pandemic was probably even higher, with the authors estimating that respiratory mortality was about ten times higher than laboratory-confirmed mortality [4]. The mortality and morbidity associated with epidemic and pandemic influenza puts a heavy burden on hospitals, long-term care units, community clinics, and national health organizations by increasing admission rates. In South Africa, the mean annual economic burden of influenza was estimated at US$270.5 million, while the mean annual burden per capita was US$5.1. The cost per capita in South Africa was lower than it was in European countries and the USA, but was similar to costs per capita in middle-income Asian countries [6]
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