川田 奈緒子

PURPOSE: As of March 2023, the number of patients with COVID-19 worldwide is declining, but the early diagnosis of patients requiring inpatient treatment and the appropriate allocation of limited healthcare resources remain unresolved issues. In this study we constructed a deep-learning (DL) model to predict the need for oxygen supplementation using clinical information and chest CT images of patients with COVID-19. MATERIALS AND METHODS: We retrospectively enrolled 738 patients with COVID-19 for whom clinical information (patient background, clinical symptoms, and blood test findings) was available and chest CT imaging was performed. The initial data set was divided into 591 training and 147 evaluation data. We developed a DL model that predicted oxygen supplementation by integrating clinical information and CT images. The model was validated at two other facilities (n = 191 and n = 230). In addition, the importance of clinical information for prediction was assessed. RESULTS: The proposed DL model showed an area under the curve (AUC) of 89.9% for predicting oxygen supplementation. Validation from the two other facilities showed an AUC > 80%. With respect to interpretation of the model, the contribution of dyspnea and the lactate dehydrogenase level was higher in the model. CONCLUSIONS: The DL model integrating clinical information and chest CT images had high predictive accuracy. DL-based prediction of disease severity might be helpful in the clinical management of patients with COVID-19.

PURPOSE: In COPD, exacerbation of the disorder causes a deterioration in the quality-of-life and worsens respiratory dysfunction, leading to a poor prognosis. In recent years, nutritional indices have been reported as significant prognostic factors in various chronic diseases. However, the relationship between nutritional indicators and prognosis in elderly subjects with COPD has not been investigated. PATIENTS AND METHODS: We enrolled 91 subjects who received COPD assessment tests (CAT), spirometry, blood tests, and multidetector computed tomography (MDCT). We divided the subjects into two groups according to age (<75 years (n=57) and ≥ 75 years (n=34)). The prognostic nutritional index (PNI) was used to assess immune-nutritional status and was calculated as 10 x serum albumin + 0.005 x total lymphocyte count. We then examined the relationship between PNI and clinical parameters, including exacerbation events. RESULTS: There was no significant correlation between the PNI and CAT, the FEV1%pred, or low attenuation volume percentage (LAV%). In the elderly group, there were significant differences between the groups with or without exacerbation in the CAT and PNI (p=0.008, p=0.004, respectively). FEV1%pred, neutrophil-to-lymphocyte ratio (NLR) and LAV% did not differ between the two groups. The analytical model combining CAT and PNI improved the prediction of exacerbations in the elderly subjects (p=0.0068). CONCLUSION: In elderly subjects with COPD, CAT were associated significantly with the risk of COPD exacerbation, with PNI also a potential predictor. The combined assessment of CAT and PNI may be a useful prognostic tool in subjects with COPD.