Abstract
Objective: Influenza is a highly infectious viral disease, which occurs epidemically almost every winter in Japan. Rapid screening of patients with suspected influenza in places of mass gathering is important to delay or prevent transmission of the infection. The aim of this study was to assess the effectiveness of our newly developed infection screening system that employed vital signs and percutaneous oxygen saturation (SpO2) as parameters in a clinical setting. Methods: Since SpO2 accurately reflects respiratory status during influenza virus infection, we upgraded our previous system by adding SpO2 as a new parameter to improve the screening accuracy. This system instantly measures SpO2 and vital signs (i.e., heart rate, respiration rate, and facial temperature), which automatically detects infected individuals via a neural network-based nonlinear discriminant function using these derived parameters. We tested the system on 45 patients with seasonal influenza (35.8℃ 2 as a screening parameter, we achieved superior sensitivity and NPV compared to that reported in our previous paper (sensitivity = 88%; NPV = 82%). Conclusions: Our results suggest that SpO2 is a good screening parameter that improves the accuracy of infection screening. The proposed system has the potential to efficiently identify infected individuals, thereby delaying or preventing the spread of infection during epidemic seasons.
Highlights
The first human infection with the novel avian influenza A (H7N9) virus was reported in mainland China in March 2013 [1]
Influenza viruses, such as H7N9, can cause severe pneumonia or acute respiratory distress syndrome, which results in significant morbidity and mortality [2,3]
In order to conduct rapid screening of infected individuals at places of mass gathering during the time of prevalence of highly contagious infectious diseases such as an avian influenza, we have developed a neural network-based infection screening system to monitor infection-induced alterations of SpO2 and vital signs, i.e., heart rate, respiration rate, and facial temperature, using non-invasive biosensors
Summary
The first human infection with the novel avian influenza A (H7N9) virus was reported in mainland China in March 2013 [1]. Influenza viruses, such as H7N9, can cause severe pneumonia or acute respiratory distress syndrome, which results in significant morbidity and mortality [2,3]. In order to conduct rapid screening of infected individuals at places of mass gathering during the time of prevalence of highly contagious infectious diseases such as an avian influenza, we have developed a neural network-based infection screening system to monitor infection-induced alterations of SpO2 and vital signs, i.e., heart rate, respiration rate, and facial temperature, using non-invasive biosensors. To achieve screening that is more accurate, we have developed a system that monitors the heart and respiration rate as well as facial temperature, as described in our previous studies [9,10]
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