Established the First Clinical Prediction Model Regarding the Risk of Hyperuricemia in Adult IgA Nephropathy

Objective: To construct a novel nomogram model that predicts the risk of hyperuricemia incidence in IgA nephropathy (IgAN).

Methods: Demographic and clinicopathological characteristics of 1184 IgAN patients in the First Affiliated Hospital of Zhengzhou University Hospital were collected. Univariate analysis and multivariate logistic regression were used to screen out hyperuricemia risk factors. The risk factors were used to establish a predictive nomogram model. The performance of the nomogram model was evaluated using an area under the receiver-operating characteristic curve (AUC), calibration plots, and a decision curve analysis.

Results: Independent predictors for hyperuricemia incidence risk included sex, hypoalbuminemia, hypertriglyceridemia, blood urea nitrogen (BUN), estimated glomerular filtration rate (eGFR), 24 h urinary protein (24 h TP), gross hematuria and tubular atrophy/interstitial fibrosis (T). The nomogram model exhibited moderate prediction ability with an AUC of 0.834 (95% CI 0.804-0.864). The AUC from validation reached 0.787 (95% CI 0.736-0.839). The decision curve analysis displayed that the hyperuricemia risk nomogram was clinically applicable.

Conclusion: Our novel and simple nomogram containing 8 factors may be useful in predicting hyperuricemia incidence risk in IgAN.