Currently, the most widely used screening methods for hyperuricemia (HUA) involves invasive laboratory tests, which are lacking in many rural hostipals in China. This study explores the use of non-invasive physical examinations to construct a simple prediction model for HUA. Data of 9,252 adults from July to October 2019 in the Affiliated Hospital of Guilin Medical College were collected and divided randomly into a training set (n = 6,364) and a validation set (n = 2,888) at a ratio of 7:3. In the training set, non-invasive physical examination indicators of age, gender, body mass index (BMI) and prevalence of hypertension were included for logistic regression analysis, and a nomogram model was established. The classification and regression tree (CART) algorithm of the decision tree model was used to build a classification tree model. Receiver operating characteristic (ROC) curve, calibration curve and decision curve analyses (DCA) were used to test the distinction, accuracy and clinical applicability of the two models. The results showed age, gender, BMI and prevalence of hypertension were all related to the occurrence of HUA. The area under the ROC curve (AUC) of the nomogram model was 0.806 and 0.791 in training set and validation set, respectively. The AUC of the classification tree model was 0.802 and 0.794 in the two sets, respectively, but were not statistically different. The calibration curves and DCAs of the two models performed well on accuracy and clinical practicality, which suggested these models may be suitable to predict HUA for rural setting.
Objectives: To evaluate the relationship between systemic lupus erythematosus (SLE) and the risk of retinal vasculitis (RV) using a population-based database.Methods: Using the 1997–2013 Taiwanese National Health Insurance Database, we identified newly diagnosed SLE patients between 2001 and 2012 as the SLE group. We matched the SLE group with non-SLE individuals selected from a representative one million sample of the population in a 1:20 ratio for age, sex, and the year of the index date. After adjusting for potential confounders, including urbanization of the patient's residence, the level of the payroll-related insured amount, and selected comorbidities, we examined the association between SLE and the risk of RV using the Cox proportional hazard model shown as hazard ratios (HRs) with 95% confidence intervals (CIs). Sensitivity analyses were conducted using various definitions of RV.Results: We included 11,586 patients with SLE and 231,720 matched non-SLE individuals. The mean age of the study participants was 36.7 ± 16.9 years, and the female-to-male ratio was 6.8:1. The incidence rates of RV were 56.39 cases per 100,000 person-years and 2.45 cases per 100,000 person-years, respectively. After adjusting for potential confounders, the incidence rate of RV in the SLE cohort was 22.99 times higher than that in the non-SLE cohort (56.39 vs. 2.45 per 100,000 person-years). The adjusted HR for RV in the SLE group was 23.61 (95% CI, 14.94–37.32). The results remained robust in the sensitivity analysis.Conclusion: This nationwide population-based study revealed that SLE patients had a significantly higher risk of RV than non-SLE individuals.
Background: The study of regulatory B cells (Bregs) in systemic lupus erythematosus (SLE) has been in full swing in recent years, but the number and function of Bregs in SLE patients have also present quite contradictory results. Therefore, we conducted a meta-analysis to verify the changes in Bregs in active SLE. Methods: We identified studies reporting the proportions of Bregs in SLE patients by searching Pubmed, Embase, Web of Science, Cochrane and CNKI. Due to the degree of heterogeneity is very high, we used a random effects model to assess the mean differences in percentages of Bregs between active SLE and controls. Then, sensitivity analysis and subgroup analysis were performed to verify potential sources of heterogeneity. Results: Seven eligible articles involving 301 active SLE patients and 218 controls were included in the meta-analysis. The pooled percentages of Bregs were found no significant difference between active SLE patients and healthy controls [0.259, (−1.150, 1.668), p = 0.719], with great heterogeneity ( I2 = 97.5%) . The result of sensitivity analysis showed that exclusion of any single study or single article did not materially resolve the heterogeneity, but after excluding the article conducted by Cai X and his colleagues, the percentages of Bregs were significantly higher in active SLE than those in controls [1.394, (0.114,2.675), p = 0.033]. The results of subgroup analysis revealed that when the disease activity was judged by SLEDAI score ≥ 5, the percentages of Bregs were significantly lower in the SLE groups than in the control groups[-1.99,(-3.241,-0.739), p = 0.002], but when the threshold of SLEDAI score ≥ 6 chosen for active SLE, the percentages of Bregs were significantly increased in the SLE groups[2.546,(1.333,3.759), p < 0.001]. Meanwhile, other subgroup analysis based on the different phenotypes of Bregs, diagnostic criteria, enrolled research countries, treatment status, and organ involvement did not differ in proportion of Bregs between SLE patients and controls. Conclusions: The study implies that Bregs may play a role in the pathogenesis of active SLE, and the thresholds of SLEDAI score to distinguish between active and inactive SLE patients are important factors affecting the percentages of Bregs.
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