Incidence of rheumatic diseases during the COVID-19 pandemic in South Korea.

BACKGROUND/AIMS: The recent coronavirus disease 2019 (COVID-19) pandemic has been associated with changes in the epidemiology of not only infectious diseases but also several non-infectious conditions. This study investigated changes in the recorded incidence of various rheumatic diseases during the COVID-19 pandemic. METHODS: The number of patients for each disease from January 2016 to December 2020 was obtained from the Korean Health Insurance Review and Assessment Service database. We compared the incidence of nine rheumatic diseases (seropositive rheumatoid arthritis, systemic lupus erythematosus [SLE], idiopathic inflammatory myositis [IIM], ankylosing spondylitis [AS], systemic sclerosis, Sjögren's syndrome, Behçet's disease [BD], polymyalgia rheumatica, and gout) and hypertensive diseases to control for changes in healthcare utilisation before and after the COVID-19 outbreak. The disease incidence before and after the COVID-19 outbreak was compared using the autoregressive integrated moving average (ARIMA) and quasi- Poisson analyses. RESULTS: Compared with the predicted incidence in 2020 using the ARIMA model, the monthly incidence of SLE, BD, AS, and gout temporarily significantly decreased, whereas other rheumatic diseases and hypertensive diseases were within the 95% confidence interval (CI) of the predicted values in the first half of 2020. In age- and sex-adjusted quasi-Poisson regression analysis, the annual incidences of IIM (rate ratio [RR], 0.473; 95% CI, 0.307 to 0.697), SLE (RR, 0.845; 95% CI, 0.798 to 0.895), and BD (RR, 0.850; 95% CI, 0.796 to 0.906) were significantly decreased compared with those in the previous 4 years. CONCLUSION: The recorded annual incidence of some rheumatic diseases, including IIM, SLE, and BD, decreased during the COVID-19 pandemic.

Differences in trajectory of disease activity according to biologic and targeted synthetic disease-modifying anti-rheumatic drug treatment in patients with rheumatoid arthritis.

BACKGROUND: The purpose of this study was to stratify patients with rheumatoid arthritis (RA) according to the trend of disease activity by trajectory-based clustering and to identify contributing factors for treatment response to biologic and targeted synthetic disease-modifying anti-rheumatic drugs (DMARDs) according to trajectory groups. METHODS: We analyzed the data from a nationwide RA cohort from the Korean College of Rheumatology Biologics and Targeted Therapy registry. Patients treated with second-line biologic and targeted synthetic DMARDs were included. Trajectory modeling for clustering was used to group the disease activity trend. The contributing factors using the machine learning model of SHAP (SHapley Additive exPlanations) values for each trajectory were investigated. RESULTS: The trends in the disease activity of 688 RA patients were clustered into 4 groups: rapid decrease and stable disease activity (group 1, n = 319), rapid decrease followed by an increase (group 2, n = 36), slow and continued decrease (group 3, n = 290), and no decrease in disease activity (group 4, n = 43). SHAP plots indicated that the most important features of group 2 compared to group 1 were the baseline erythrocyte sedimentation rate (ESR), prednisolone dose, and disease activity score with 28-joint assessment (DAS28) (SHAP value 0.308, 0.157, and 0.103, respectively). The most important features of group 3 compared to group 1 were the baseline ESR, DAS28, and estimated glomerular filtration rate (eGFR) (SHAP value 0.175, 0.164, 0.042, respectively). The most important features of group 4 compared to group 1 were the baseline DAS28, ESR, and blood urea nitrogen (BUN) (SHAP value 0.387, 0.153, 0.144, respectively). CONCLUSIONS: The trajectory-based approach was useful for clustering the treatment response of biologic and targeted synthetic DMARDs in patients with RA. In addition, baseline DAS28, ESR, prednisolone dose, eGFR, and BUN were important contributing factors for 4-year trajectories.

Machine learning model for identifying important clinical features for predicting remission in patients with rheumatoid arthritis treated with biologics.

BACKGROUND: We developed a model to predict remissions in patients treated with biologic disease-modifying anti-rheumatic drugs (bDMARDs) and to identify important clinical features associated with remission using explainable artificial intelligence (XAI). METHODS: We gathered the follow-up data of 1204 patients treated with bDMARDs (etanercept, adalimumab, golimumab, infliximab, abatacept, and tocilizumab) from the Korean College of Rheumatology Biologics and Targeted Therapy Registry. Remission was predicted at 1-year follow-up using baseline clinical data obtained at the time of enrollment. Machine learning methods (e.g., lasso, ridge, support vector machine, random forest, and XGBoost) were used for the predictions. The Shapley additive explanation (SHAP) value was used for interpretability of the predictions. RESULTS: The ranges for accuracy and area under the receiver operating characteristic of the newly developed machine learning model for predicting remission were 52.8-72.9% and 0.511-0.694, respectively. The Shapley plot in XAI showed that the impacts of the variables on predicting remission differed for each bDMARD. The most important features were age for adalimumab, rheumatoid factor for etanercept, erythrocyte sedimentation rate for infliximab and golimumab, disease duration for abatacept, and C-reactive protein for tocilizumab, with mean SHAP values of - 0.250, - 0.234, - 0.514, - 0.227, - 0.804, and 0.135, respectively. CONCLUSIONS: Our proposed machine learning model successfully identified clinical features that were predictive of remission in each of the bDMARDs. This approach may be useful for improving treatment outcomes by identifying clinical information related to remissions in patients with rheumatoid arthritis.