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We investigated risk factors for immune-related adverse events (irAEs) in patients treated with anti-programmed cell death protein1 antibody pembrolizumab. A retrospective medical record review was performed to identify all patients who received at least one dose of pembrolizumab at Samsung Medical Center, Seoul, Korea between June 2015 and December 2017. Three hundred and ninety-one patients were included in the study. Data were collected on baseline characteristics, treatment details, and adverse events. Univariate and multivariate logistic regression models were used to identify risk factors for irAEs. Sixty-seven (17.1%) patients experienced clinically significant irAEs; most commonly dermatologic disorders, followed by pneumonitis, musculoskeletal disorders, and endocrine disorders. Fourteen patients (3.6%) experienced serious irAEs (grade ≥ 3). Most common serious irAEs were pneumonitis (2.3%). Four deaths were associated with irAEs, all of which were due to pneumonitis. In multivariate regression analysis, a higher body mass index (BMI) and multiple cycles of pembrolizumab were associated with higher risk of irAEs (BMI: odds ratio [OR] 1.08, 95% confidence interval [CI] 1.01–1.16; pembrolizumab cycle: OR 1.15, 95% CI 1.08–1.22). A derived neutrophil-lymphocyte ratio (dNLR) greater than 3 at baseline was correlated with low risk of irAEs (OR 0.37, 95% CI 0.17–0.81). Our study demonstrated that an elevated BMI and higher number of cycles of pembrolizumab were associated with an increased risk of irAEs in patients treated with pembrolizumab. Additionally, increased dNLR at baseline was negatively correlated with the risk of developing irAEs.

Background Few studies on rheumatoid arthritis (RA) have generated machine learning models to predict biologic disease-modifying antirheumatic drugs (bDMARDs) responses; however, these studies included insufficient analysis on important features. Moreover, machine learning is yet to be used to predict bDMARD responses in ankylosing spondylitis (AS). Thus, in this study, machine learning was used to predict such responses in RA and AS patients. Methods Data were retrieved from the Korean College of Rheumatology Biologics therapy (KOBIO) registry. The number of RA and AS patients in the training dataset were 625 and 611, respectively. We prepared independent test datasets that did not participate in any process of generating machine learning models. Baseline clinical characteristics were used as input features. Responders were defined as those who met the ACR 20% improvement response criteria (ACR20) and ASAS 20% improvement response criteria (ASAS20) in RA and AS, respectively, at the first follow-up. Multiple machine learning methods, including random forest (RF-method), were used to generate models to predict bDMARD responses, and we compared them with the logistic regression model. Results The RF-method model had superior prediction performance to logistic regression model (accuracy: 0.726 [95% confidence interval (CI): 0.725–0.730] vs. 0.689 [0.606–0.717], area under curve (AUC) of the receiver operating characteristic curve (ROC) 0.638 [0.576–0.658] vs. 0.565 [0.493–0.605], F1 score 0.841 [0.837–0.843] vs. 0.803 [0.732–0.828], AUC of the precision-recall curve 0.808 [0.763–0.829] vs. 0.754 [0.714–0.789]) with independent test datasets in patients with RA. However, machine learning and logistic regression exhibited similar prediction performance in AS patients. Furthermore, the patient self-reporting scales, which are patient global assessment of disease activity (PtGA) in RA and Bath Ankylosing Spondylitis Functional Index (BASFI) in AS, were revealed as the most important features in both diseases. Conclusions RF-method exhibited superior prediction performance for responses of bDMARDs to a conventional statistical method, i.e., logistic regression, in RA patients. In contrast, despite the comparable size of the dataset, machine learning did not outperform in AS patients. The most important features of both diseases, according to feature importance analysis were patient self-reporting scales.

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.

Background Previous studies have shown that the incidence and risk factors of gout differs according to sex. However, little research has been done on the association between reproductive factors and gout. We conducted an analysis of a large nationwide population-based cohort of postmenopausal women to determine whether there is an association between reproductive factors and the incidence of gout. Methods A total of 1,076,378 postmenopausal women aged 40–69 years who participated in national health screenings in 2009 were included in the study. The outcome was the occurrence of incident gout, which was defined using the ICD-10 code of gout (M10) in the claim database. Cox proportional hazard models were used for the analyses and stratified analyses according to body mass index (BMI) and the presence/absence of chronic kidney disease (CKD) were performed. Results The mean follow-up duration was 8.1 years, and incident cases of gout were 64,052 (incidence rate 7.31 per 1000 person-years). Later menarche, earlier menopause, and a shorter reproductive span were associated with a high risk of gout. No association between parity and gout incidence was observed. Use of oral contraceptives (OC) and hormone replacement therapy (HRT) were associated with an increased risk of gout. The association between reproductive factors and gout was not statistical significant in the high BMI group. The effects of OC and HRT usage on gout were not significant in the CKD group. Conclusion Shorter exposure to endogenous estrogen was associated with a high risk of gout. Conversely, exposure to exogenous estrogen such as OC and HRT was associated with an increased risk of gout.

This study aimed to evaluate the relative risk of malignancy in patients with Takayasu’s arteritis compared to that in the general population. This retrospective nationwide cohort study used data from the Korean Health Insurance Review and Assessment Service database. All newly diagnosed patients with Takayasu’s arteritis were identified between January 2009 and December 2019. They were observed until the diagnosis of malignancy, death, or end of the observational period, December 2020. The standardized incidence ratios (SIRs) of the overall and site-specific malignancies were estimated and compared with the incidence of cancer in the general population retrieved from the National Cancer Registry. We identified 1449 newly diagnosed patients with Takayasu’s arteritis during the observational period (9196 person-years). A total of 74, 66, and 8 patients had overall, solid, and hematologic malignancies, respectively. The risks of overall [SIR, 1.62; 95% confidence interval (CI) 1.27–2.03], solid (SIR, 1.51; 95% CI 1.17–1.92), and hematologic (SIR, 4.05; 95% CI 1.75–7.98) malignancies were increased compared to those in the general population. In solid malignancies, breast (SIR, 2.07; 95% CI 1.16–3.42) and ovarian (SIR, 4.45; 95% CI 1.21–11.39) cancers had an increased risk. In hematologic malignancies, the risk of myelodysplasia increased (SIR, 18.02; 95% CI 3.72–52.66). Immunosuppressive agent use was not associated with malignancy. There was no specific period when cancer more frequently occurred. An increased risk of malignancy was observed in patients with Takayasu’s arteritis compared to that in the general population in this large-scale nationwide population study of Korean health insurance data.

This study aimed to evaluate the prevalence of comorbidities in patients with rheumatoid arthritis (RA) compared with the non-RA population. The 2010-2012 Korea National Health and Nutrition Examination Survey (KNHANES), which assesses the general health status of populations in South Korea using interviews and basic health assessment, was analyzed retrospectively. Weighted prevalence and odds ratio (OR) of comorbidities were analyzed in patients with RA compared with the non-RA population. The overall weighted (n = 37,453,158) prevalence of RA was 1.5%. Patients with RA were older and more female predominant than subjects without RA. The prevalence of living in an urban area, college graduation, alcohol consumption and smoking was lower in patients with RA than non-RA. Patients with RA had more comorbidities including hypertension, dyslipidemia, myocardial infarction (MI) or angina, stoke, osteoarthritis, lung cancer, colon cancer, pulmonary tuberculosis, asthma, diabetes, depression, thyroid disease and chronic kidney disease. After adjusting socioeconomic and lifestyle characteristics, RA was associated with an increased prevalence of MI or angina (OR 1.86, 95% CI 1.17-2.96, p = 0.009), pulmonary TB (OR 1.95, 95% CI 1.24-3.09, p = 0.004), asthma (OR 1.97, 95% CI 1.05-3.71, p = 0.036), thyroid disease (OR 1.71, 95% CI 1.05-2.77), depression (OR 2.38, 95% CI 1.47-3.85, p < 0.001) and hepatitis B (OR 2.34, 95% CI 1.15-4.80, p = 0.020) compared with the non-RA population. Prevalence of solid cancer was not significantly associated with RA after adjustment.

Objective To describe the clinical characteristics and radiographic outcomes of vascular Behçet’s disease (BD) involving the aorta or its major branches. Methods This retrospective cohort study was performed in patients with vascular BD involving the aorta or its major branches. All included patients underwent computed tomography angiography (CTA) at least two times with a 2- to 5-year interval. Radiographic progression was defined as newly developed and/or aggravated (> 20%) characteristic features on CTA. Results The cohort included 22 patients with BD with a median interval of 3.65 years between the initial and follow-up CTA. Five patients (22.7%) showed radiographic progression. Patients with radiographic progression had a longer disease duration at baseline than those without (6.67 vs. 0.26 years, p  = 0.028). Of all patients, 21 (95.5%) had vascular aneurysms/pseudoaneurysms and 11 (50.0%) had thrombosis. The most frequently involved artery with aneurysmal change was the abdominal aorta (8/21, 38.1%), followed by the iliac arteries (5/21, 23.8%). In the case of thrombosis, the most frequently involved arteries were the femoral (4/11, 36.4%) and iliac (4/11, 36.4%) arteries. The characteristics and locations of vascular involvement did not significantly differ according to the radiographic outcome. Conclusions A considerable proportion of patients with BD with arterial involvement showed radiographic progression within 2–5 years. Patients with radiographic progression had a longer disease duration at baseline. The most common form of arterial involvement of BD was aneurysmal change, followed by thrombus formation. Key Points: • This study evaluated for the first time the radiographic outcomes of 22 patients with Behçet’s disease involving the aorta or its major branches. • A considerable proportion of patients (5/22, 22.7%) showed radiographic progression. • Patients with radiographic progression had a longer disease duration at baseline than their counterparts; however, no other clinical factors were significantly different. • The most frequent form of vascular involvement was pseudoaneurysm followed by thrombosis.

Background This study aimed to classify the distinct group of patients with axial spondyloarthritis (SpA) on tumour necrosis factor alpha inhibitors (TNFi) according to the baseline characteristics using a clustering algorithm. Methods The clinical characteristics and demographic data of patients with axial SpA included in the Korean College of Rheumatology Biologics and Targeted Therapy registry were investigated. The patterns of disease manifestations were examined using divisive hierarchical cluster analysis. After clustering, we compared the clinical characteristics of patients and the drug survival of TNFi between the classified groups. Results A total of 1042 patients were analysed. The cluster analysis classified patients into two groups: axial group predominantly showing isolated axial manifestations ( n = 828) and extra-axial group more frequently showing extra-axial symptoms ( n = 214). Almost all extra-axial symptoms (peripheral arthritis, enthesitis, uveitis, and psoriasis) were more frequently observed in the extra-axial group than in the axial group. Moreover, patients in the extra-axial group had shorter disease duration, later disease onset, and higher disease activity than those in the axial group. The disease activity was comparable between the two groups after 1 year of treatment with TNFi. Interestingly, the extra-axial group had a lower drug survival with TNFi than the axial group ( p = 0.001). Conclusions Cluster analysis of patients with axial SpA using TNFi classified two distinct clinical phenotypes. These clusters had different TNFi drug survival, clinical characteristics, and disease activity.

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.

Background Ankylosing spondylitis (AS) is a male-predominant disease, and radiographic evidence of damage is also more severe in males. Estrogen modulates immune-related processes such as T cell differentiation and cytokine production. This study aimed to evaluate the effect of estrogen on the disease activity of spondyloarthritis (SpA). Methods The effects of estrogen on the development of arthritis were evaluated by performing ovariectomy and 17β-estradiol (E2) pellet implantation in zymosan-treated SKG mice. Clinical arthritis scores were measured, and 18 F-fluorodeoxyglucose ( 18 F-FDG) small-animal positron emission tomography/computed tomography performed to quantify joint inflammation. The expression of inflammatory cytokines in joint tissue was measured. Results E2-treated mice showed remarkable suppression of arthritis clinically and little infiltration of inflammatory cells in the Achilles tendon and intervertebral disc. 18 F-FDG uptake was significantly lower in E2-treated mice than in sham-operated (sham) and ovariectomized mice. Expression of TNF, interferon-γ, and IL-17A was significantly reduced in E2-treated mice, whereas expression of sclerostin and Dickkopf-1 was increased in E2-treated mice compared with sham and ovariectomized mice. Conclusions Estrogen suppressed arthritis development in SKG mice, a model of SpA. Results of this study suggest that estrogen has an anti-inflammatory effect on the spondyloarthritis manifestations of the SKG arthritis model.