Clinical predictive model for the 1-year remission probability of IgA vasculitis nephritis.

Purpose: Early remission of Immunoglobulin A vasculitis nephritis (IgAVN) substantially affects its prognosis. In this work, a multivariate model to predict the 1-year remission probability of patients with IgAVN was developed on the basis of clinical laboratory data.

Methods: Data of 187 patients with IgAVN confirmed by renal biopsy were retrospectively assessed. Least absolute shrinkage and selection operator regression analysis were conducted to establish a multivariate logistic regression model. A nomogram based on the multivariate logistic regression model was constructed for easy application in clinical practice. Concordance index, receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and clinical impact curves (CIC) were used to evaluate the predictive accuracy and clinical value of this nomogram.

Results: The predictive factors contained in the multivariate model included duration, gender, respiratory infection, arthritis, edema, estimated glomerular filtration rate, 24 h urine protein, uric acid, and renal ultrasound intensity. The area under the curves (AUC) of the nomogram in the training set and testing set were 0.814 and 0.822, respectively, indicating its good predictive ability. Moreover, the DCA curve and CIC revealed its clinical utility.

Conclusion: The developed multivariate predictive model combines the clinical and laboratory factors of patients with IgAVN and is useful in the individualized prediction of the 1-year remission probability aid for clinical decision-making during treatment and management of IgAVN.