ABSTRACT
BACKGROUND: Quantitative evaluation of radiographic images has been developed and suggested for the diagnosis of coronavirus disease 2019 (COVID-19). However, there are limited opportunities to use these image-based diagnostic indices in clinical practice. Our aim in this study was to evaluate the utility of a novel visually-based classification of pulmonary findings from computed tomography (CT) images of COVID-19 patients with the following three patterns defined: peripheral, multifocal, and diffuse findings of pneumonia. We also evaluated the prognostic value of this classification to predict the severity of COVID-19. METHODS: This was a single-center retrospective cohort study of patients hospitalized with COVID-19 between January 1st and September 30th, 2020, who presented with suspicious findings on CT lung images at admission (n = 69). We compared the association between the three predefined patterns (peripheral, multifocal, and diffuse), admission to the intensive care unit, tracheal intubation, and death. We tested quantitative CT analysis as an outcome predictor for COVID-19. Quantitative CT analysis was performed using a semi-automated method (Thoracic Volume Computer-Assisted Reading software, GE Health care, United States). Lungs were divided by Hounsfield unit intervals. Compromised lung (%CL) volume was the sum of poorly and non-aerated volumes (- 500, 100 HU). We collected patient clinical data, including demographic and clinical variables at the time of admission. RESULTS: Patients with a diffuse pattern were intubated more frequently and for a longer duration than patients with a peripheral or multifocal pattern. The following clinical variables were significantly different between the diffuse pattern and peripheral and multifocal groups: body temperature (p = 0.04), lymphocyte count (p = 0.01), neutrophil count (p = 0.02), c-reactive protein (p < 0.01), lactate dehydrogenase (p < 0.01), Krebs von den Lungen-6 antigen (p < 0.01), D-dimer (p < 0.01), and steroid (p = 0.01) and favipiravir (p = 0.03) administration. CONCLUSIONS: Our simple visual assessment of CT images can predict the severity of illness, a resulting decrease in respiratory function, and the need for supplemental respiratory ventilation among patients with COVID-19.
Subject(s)
COVID-19/classification , COVID-19/diagnostic imaging , Tomography, X-Ray Computed , Adult , Aged , Amides/therapeutic use , Antiviral Agents/therapeutic use , Body Temperature , C-Reactive Protein/metabolism , COVID-19/physiopathology , Female , Fibrin Fibrinogen Degradation Products/metabolism , Humans , L-Lactate Dehydrogenase/blood , Lung/diagnostic imaging , Lymphocyte Count , Male , Middle Aged , Mucin-1/blood , Neutrophils , Predictive Value of Tests , Prognosis , Pyrazines/therapeutic use , Radiographic Image Interpretation, Computer-Assisted , Retrospective Studies , SARS-CoV-2 , Steroids/therapeutic use , COVID-19 Drug TreatmentABSTRACT
BACKGROUND: The outbreak of COVID-19 has caused mental health problems and increased unemployment due to the economic recession. This survey aimed to assess the psychological impact of the state of emergency. We estimated changes in mental health, quality of life, and unemployment experience for general workers during the first COVID-19 outbreak in Japan. METHODS: We conducted a nationwide follow-up study. During the periods of March 26 to April 6, 2020 and June 26 to July 2, 2020, we used the internet to survey general workers aged 15 to 59 years in Japan. The questionnaire items covered employment status and socioeconomic factors, and we used the Center for Epidemiologic Studies Depression Scale (CES-D) and EQ-5D-5L to assess depression and health-related quality of life (HR-QOL), respectively. The differences in outcomes of permanent and non-permanent workers were analyzed using propensity score analysis. A multiple linear regression analysis was performed to examine the relationship between unemployment and CES-D scores. RESULTS: We included 2351 subjects in the analysis. Changes in both CES-D scores and utility were not significantly different between the two groups. However, a significant difference was found regarding the rate of unemployment, which was associated with higher CES-D scores. CONCLUSIONS: The present study demonstrated that the mental health of non-permanent workers was not negatively affected following the state of emergency due to COVID-19 in Japan. Unemployment is an important factor that influences the mental health of general workers.