Objective Despite the high incidence and mortality of cardiovascular events in hyperuricemia patients, the role of serum uric acid in cardiovascular diseases is still controversial. The aim of this meta-analysis was to explore the difference of carotid intima-media thickness in hyperuricemia and control groups. Methods We performed this meta-analysis by searching the PubMed, Cochrane Library, Embase and Web of Science databases up to July 2020. The 95% confidence intervals and standard mean differences were calculated to analyze the differences in carotid intima-media thickness in hyperuricemia groups and control groups. Sensitivity analysis, subgroup analysis and meta-regression were used to explore the sources of heterogeneity. Publication bias was evaluated by funnel plot and Begg's regression test. We used Stata 14.0 software to complete our analyses. Results A total of 8 articles were included. The results showed that there was a significant increase in carotid intima-media thickness in the hyperuricemia groups compared with the control groups [SMD = 0.264, 95% CI (0.161-0.366), P < 0.001]. Subgroup analyses showed that age, sample size, blood pressure and body mass index were not the source of heterogeneity. Meta-regression enrolled the method of CIMT measurement, location, age, smoking and diabetes mellitus as categorical variables, but none of these factors was found to be significant in the model. The Begg's test value (P = 0.174) was greater than 0.05, indicating there was no publication bias.
ConclusionThe results showed that carotid intima-media thickness was increased in hyperuricemia patients compared with controls, which indicated that hyperuricemia patients may have a higher risk of cardiovascular diseases.
ObjectivesTo investigate whether machine learning, which is widely used in disease prediction and diagnosis based on demographic data and serological markers, can predict herpes occurrence in patients with systemic lupus erythematosus (SLE).MethodsA total of 286 SLE patients were included in this study, including 200 SLE patients without herpes and 86 SLE patients with herpes. SLE patients were randomly divided into a training group and a test group, and 18 demographic characteristics and serological indicators were compared between the two groups.ResultsWe selected basophil, monocyte, white blood cell, age, immunoglobulin E, SLE Disease Activity Index, complement 4, neutrophil, and immunoglobulin G as the basic features of modeling. A random forest model had the best performance, but logistic and decision tree analyses had better clinical decision‐making benefits. Random forest had a good consistency between feature importance judgment and feature selection. The 10‐fold cross‐validation showed the optimization of five model parameters.ConclusionThe random forest model may be an excellently performing model, which may help clinicians to identify SLE patients whose disease is complicated by herpes early.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.