Epidemiologic studies suggest that COPD is associated with an increased risk of poor outcome in patients with COVID-19, although they failed to demonstrate COPD as a risk factor for acquiring COVID-19. However, most data have come from a limited global population. In this nationwide cohort study based on the Korean national health insurance claims-based database, COPD is associated with increased risk for COVID-19 and having COPD does not seem to confer substantial risk for severe COVID-19 and mortality. These findings indicate that heterogeneity in the populations across many countries may complicate the net effects of COPD on the COVID-19-related outcomes.

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Objectives: To investigate the factors related to the severity and mo rtality of COVID-19 using big data-machine learning techniques. Methods: This study included 8070 patients in South Korea diagnosed with COVID-19 between January and July 2020, and whose data were available from the National-Health-Insurance-Service. Results: Machine-learning algorithms were performed to evaluate the effects of comorbidities on severity and mortality of COVID-19. The most common comorbidities of COVID-19 were pulmonary inflammation followed by hypertension. The model that best predicted severity was a neural network (AUC: 85.06%). The most important variable for predicting severity in the neural network model was a history of influenza (relative importance: 0.083). The model that best predicted mortality was the logistic regression elastic net (EN) model (AUC: 93.86%). The most important variables for mortality in the EN model were age (coefficient: 2.136) and anosmia (coefficient: –1.438). Conclusions: In COVID-19 patients, influenza was found to be a major adverse factor in addition to old age and male. In addition, anosmia was found to be a major factor associated with lower severity and mortality. Therefore, in the current situation where there is no adequate COVID-19 treatment at present, examining the patient's history of influenza vaccination and anosmia in addition to age and sex will be an important indicator for predicting the severity and mortality of COVID-19 patients.

Aim The incidence of fungal sinusitis is increasing; however, its pathophysiology has not been investigated previously. We investigate the effect of periodontitis on the incidence of fungal sinusitis over a 12‐year follow‐up period using nationwide population‐based data. Materials and Methods The periodontitis group was randomly selected from the National Health Insurance Service database. The non‐periodontitis group was obtained by propensity score matching considering several variables. The primary end point was the diagnosis of sinonasal fungal balls (SFBs) and invasive fungal sinusitis (IFS). Results The periodontitis and non‐periodontitis groups included 12,442 and 12,442 individuals, respectively. The overall adjusted hazard ratio (aHR) for SFBs in the periodontitis group was 1.46 (p = .002). In subgroup analysis, the aHR for SFBs was 1.59 (p = 0.008) for those with underlying chronic kidney disease (CKD), 1.58 (p = .022) for those with underlying atopic dermatitis, 1.48 (p = .019) for those with chronic obstructive pulmonary disease (COPD), and 1.36 (p = .030) for those with diabetes mellitus (DM), but these values are applicable only when considering the relationship between periodontitis and SFB. The aHR for IFS in the periodontitis group was higher than in the non‐periodontitis group (2.80; p = .004). Conclusions The risk of SFBs and IFS increased after diagnosis of periodontitis. This trend is often more severe in patients with DM, COPD, or CKD, but this association with underlying diseases is applicable only when considering the association between periodontitis and fungal sinusitis.

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