Statistical analyses
We conducted propensity score matching analysis to compare outcomes between the two groups. A multivariable logistic regression model with the following variables as covariates was used to estimate propensity scores for receiving glucocorticoids on the day of admission: age, sex; body mass index category, Japan Coma Scale score (alert, drowsy, somnolent, and comatose)10; smoking status (never, past and current smoker, and missing); diagnoses (T78.0, T78.2, T88.6); Charlson Comorbidity Index score (0, 1, 2, and ≥3); history of asthma, atopic dermatitis, and atopic rhinitis; use of histamine 1 blockers, histamine 2 blockers and beta 2-adrenergic receptor stimulants; hospital volume (very low, low, high, and very high); and teaching hospital. We performed one-to-four nearest-neighbor matching with replacement for estimated propensity scores, using a caliper width set at one fifth of the standard deviation of the estimated propensity scores. To assess the accuracy of the matching, we compared the covariates before and after propensity-score matching using absolute standardized differences, absolute standardized differences ≤ 10% being considered to denote negligible imbalances between the two groups.11 After propensity score matching, we assessed the outcomes through generalized linear models, accompanied by cluster-robust standard errors with hospitals as the clusters. We calculated odds ratios and their 95% confidence intervals (CIs) with generalized linear models using the logit link function.