Influence of occupational exposure on hyperuricemia in steelworkers: a nested case-control study

Effectiveness and safety of different doses of febuxostat compared with allopurinol in the treatment of hyperuricemia: a meta-analysis of randomized controlled trials

BACKGROUND

The prevalence of hyperuricemia has increased steadily with the continuous improvement of living standards. Some studies have reported the clinical effectiveness and safety of different doses of febuxostat in comparison with allopurinol in hyperuricemia treatment, but the sample sizes of the studies have been small, and the results have been inconsistent. We designed this meta-analysis to evaluate the effectiveness and safety of different doses of febuxostat compared with allopurinol in the treatment of hyperuricemia.

METHODS

The Cochrane Library, Embase, PubMed, Web of Science and ClinicalTrials.gov databases were searched to identify randomized controlled trials (RCTs) comparing the use of febuxostat and allopurinol for the treatment of hyperuricemia. The effectiveness and safety of different doses of febuxostat and allopurinol in treating hyperuricemia were assessed using meta-analysis.

RESULTS

A total of 11 randomized controlled trials were included in the meta-analysis. The results of the meta-analysis showed that the percentage of patients achieving serum uric acid levels of 6.0 mg/dL or less was higher among patients taking febuxostat (80 mg/d) than among patients taking allopurinol (200-300 mg/d) [RR = 1.79, 95% CI (1.55, 2.08), P < 0.00001]. However, there was no statistically significant difference in the percentage of patients achieving serum uric acid levels of 6.0 mg/dL or less between febuxostat (40 mg/d) and allopurinol (200-300 mg/d) [RR = 1.10, 95% CI (0.93, 1.31), P = 0.25]. There was also no statistically significant difference in the incidence of gout between the febuxostat (40 mg/d) and allopurinol (200-300 mg/d) [RR = 0.97, 95% CI (0.64, 1.49), P = 0.91] or between the febuxostat (80 mg/d) and allopurinol (200-300 mg/d) [RR = 1.13, 95% CI (0.81, 1.58), P = 0.48].No significant difference in the incidence of major adverse reactions as observed between the febuxostat (40 mg/d) and allopurinol (200-300 mg/d) [RR = 1.16; 95% CI (0.43, 3.16), P = 0.77] or between the febuxostat (80 mg/d) and allopurinol (200-300 mg/d) [RR = 1.06; 95% CI (0.79, 1.42), P = 0.70]. The incidence of adverse cardiovascular events did not differ significantly between the febuxostat (40 mg/d) and allopurinol (200-300 mg/d) [RR = 1.30; 95% CI (0.57, 2.95), P = 0.53] or between the febuxostat (80 mg/d) and allopurinol (200-300 mg/d) [RR = 1.79; 95% CI (0.74, 4.32), P = 0.20].

CONCLUSIONS

Febuxostat (80 mg/d) was associated with a higher percentage of patients achieving serum uric acid levels of 6.0 mg/dL or less than allopurinol (200-300 mg/d), however, febuxostat (80 mg/d) did not exhibit better efficacy in reducing the incidence of gout. More attention should be devoted to the adverse reactions caused by an increase in febuxostat doses.

Risk Prediction for the Development of Hyperuricemia: Model Development Using an Occupational Health Examination Dataset

OBJECTIVE

Hyperuricemia has become the second most common metabolic disease in China after diabetes, and the disease burden is not optimistic.

METHODS

We used the method of retrospective cohort studies, a baseline survey completed from January to September 2017, and a follow-up survey completed from March to September 2019. A group of 2992 steelworkers was used as the study population. Three models of Logistic regression, CNN, and XG Boost were established to predict HUA incidence in steelworkers, respectively. The predictive effects of the three models were evaluated in terms of discrimination, calibration, and clinical applicability.

RESULTS

The training set results show that the accuracy of the Logistic regression, CNN, and XG Boost models was 84.4, 86.8, and 86.6, sensitivity was 68.4, 72.3, and 81.5, specificity was 82.0, 85.7, and 86.8, the area under the ROC curve was 0.734, 0.724, and 0.806, and Brier score was 0.121, 0.194, and 0.095, respectively. The XG Boost model effect evaluation index was better than the other two models, and similar results were obtained in the validation set. In terms of clinical applicability, the XG Boost model had higher clinical applicability than the Logistic regression and CNN models.

CONCLUSION

The prediction effect of the XG Boost model was better than the CNN and Logistic regression models and was suitable for the prediction of HUA onset risk in steelworkers.