Risk assessment and prediction model of renal damage in childhood immunoglobulin A vasculitis - Fu, Ruqian; Yang, Manqiong; Li, Zhihui; Kang, Zhijuan; Xun, Mai; Wang, Ying; Wang, Manzhi; Wang, Xiangyun (Frontiers in Pediatrics, 2022)
Objectives: To explore the risk factors for renal damage in childhood immunoglobulin A vasculitis (IgAV) and construct a clinical model for individual risk prediction and assessment of benefits for patients. Methods: We retrospectively analyzed the clinical data of 1007 children in our hospital and 287 children in other hospitals who were diagnosed with IgAV. Approximately 70% of the cases in our hospital were randomly selected using statistical product service soltions (SPSS) software for modeling. The remaining 30% of the cases were selected for internal verification, and the other hospital’s cases were reviewed for external verification. A clinical prediction model for renal damage in children with IgAV was constructed by analyzing the modeling data through single-factor and multiple-factor logistic regression analyses. Then, we assessed and verified the degree of discrimination, calibration and clinical usefulness of the model. Finally, the prediction model was rendered in the form of a nomogram. Results: Age, persistent cutaneous purpura, erythrocyte distribution width, complement C3, immunoglobulin G and triglycerides were independent influencing factors of renal damage in IgAV. Based on these factors, the area under the curve (AUC) for the prediction model was 0.772; the calibration curve did not significantly deviate from the ideal curve; and the clinical decision curve was higher than two extreme lines when the prediction probability was approximately 15%−82%. When the internal and external verification datasets were applied to the prediction model, the AUC was 0.729 and 0.750, respectively, and the Z test was compared with the modeling AUC, P > 0.05. The calibration curves fluctuated around the ideal curve, and the clinical decision curve was higher than two extreme lines when the prediction probability was 25%~84% and 14%~73%, respectively. Conclusion: The prediction model has a good degree of discrimination, calibration and clinical usefulness. Either the internal or external verification has better clinical efficacy, indicating that the model has repeatability and portability.