Despite a growing burden posed by cardiovascular disease (CVD) in rheumatoid arthritis (RA) patients, large-scale studies on the association between the characteristics of RA patients and CVD risks and studies adjusted for various confounding factors are lacking. In this large-scale nationwide cohort study, we aimed to investigate the association between CVD risk and RA and factors that may increase CVD risk using a dataset provided by the Korean National Health Insurance Service (NHIS). We enrolled 136,469 patients with RA who participated in national health examinations within two years of RA diagnosis between 2010 and 2017 and non-RA controls matched by age and sex (n = 682,345). The outcome was the occurrence of myocardial infarction (MI) or stroke. MI was defined as one hospitalization or two outpatient visits with ICD-10-CM codes I21 or I22. Stroke was defined as one hospitalization with ICD-10-CM codes I63 or I64 and a claim for brain imaging (CT or MRI). The Cox proportional hazard model and Kaplan–Meier curve were used for analysis. The mean follow-up duration was 4.7 years, and the incidence rate of CVD was higher in the RA group than the control group (MI: 3.20 vs. 2.08; stroke: 2.84 vs. 2.33 per 1000 person-years). The risk of MI and stroke was about 50% and 20% higher, respectively, in RA patients. The association between RA and CVD was prominent in females after adjusting for confounding variables. The association between RA and risk of MI was significant in individuals without DM. Therefore, appropriate screening for CVD is important in all RA patients including females and younger patients.

We aim to generate an artificial neural network (ANN) model to predict early TNF inhibitor users in patients with ankylosing spondylitis. The baseline demographic and laboratory data of patients who visited Samsung Medical Center rheumatology clinic from Dec. 2003 to Sep. 2018 were analyzed. Patients were divided into two groups: early-TNF and non-early-TNF users. Machine learning models were formulated to predict the early-TNF users using the baseline data. Feature importance analysis was performed to delineate significant baseline characteristics. The numbers of early-TNF and non-early-TNF users were 90 and 505, respectively. The performance of the ANN model, based on the area under curve (AUC) for a receiver operating characteristic curve (ROC) of 0.783, was superior to logistic regression, support vector machine, random forest, and XGBoost models (for an ROC curve of 0.719, 0.699, 0.761, and 0.713, respectively) in predicting early-TNF users. Feature importance analysis revealed CRP and ESR as the top significant baseline characteristics for predicting early-TNF users. Our model displayed superior performance in predicting early-TNF users compared with logistic regression and other machine learning models. Machine learning can be a vital tool in predicting treatment response in various rheumatologic diseases.

Hyperuricemia and anemia share several comorbidities, but the association between the two conditions remains unclear. The purpose of this study was to investigate the association between hyperuricemia and anemia. Data of 10794 subjects from the Korean National Health and Nutrition Examination Survey conducted in 2016–2017 were analyzed using multivariate logistic regression analyses. An association between anemia and hyperuricemia was not evident in subjects without chronic kidney disease (CKD). In patients with CKD, anemia increased the risk of hyperuricemia by 2-fold. This association remained significant when adjusting for the glomerular filtration rate. In subgroup analyses, the association of anemia with hyperuricemia was significant in subjects aged ≥65 years, and in those with diabetes or hypertension. Subgroup analyses of CKD patients showed similar results. In the current study using data from Korean representative samples, anemia in subjects with CKD was associated with a 2-fold increase in the risk of hyperuricemia, which remained significant even after adjustment for renal function.

Objectives Summarise the evidence of the performance of the machine learning algorithm in discriminating sacroiliitis features on MRI and compare it with the accuracy of human physicians. Methods MEDLINE, EMBASE, CIHNAL, Web of Science, IEEE, American College of Rheumatology and European Alliance of Associations for Rheumatology abstract archives were searched for studies published between 2008 and 4 June 2023. Two authors independently screened and extracted the variables, and the results are presented using tables and forest plots. Results Ten studies were selected from 2381. Over half of the studies used deep learning models, using Assessment of Spondyloarthritis International Society sacroiliitis criteria as the ground truth, and manually extracted the regions of interest. All studies reported the area under the curve as a performance index, ranging from 0.76 to 0.99. Sensitivity and specificity were the second-most commonly reported indices, with sensitivity ranging from 0.56 to 1.00 and specificity ranging from 0.67 to 1.00; these results are comparable to a radiologist’s sensitivity of 0.67–1.00 and specificity of 0.78–1.00 in the same cohort. More than half of the studies showed a high risk of bias in the analysis domain of quality appraisal owing to the small sample size or overfitting issues. Conclusion The performance of machine learning algorithms in discriminating sacroiliitis features on MRI varied owing to the high heterogeneity between studies and the small sample sizes, overfitting, and under-reporting issues of individual studies. Further well-designed and transparent studies are required.