Risk assessment and prediction model of renal damage in childhood immunoglobulin A vasculitis

• Main Authors: Ruqian Fu (Author), Manqiong Yang (Author), Zhihui Li (Author), Zhijuan Kang (Author), Mai Xun (Author), Ying Wang (Author), Manzhi Wang (Author), Xiangyun Wang (Author)
• Format: Book
• Published: Frontiers Media S.A., 2022-08-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2296-2360; 10.3389/fped.2022.967249
• Keywords: children; immunoglobulin vasculitis; renal damage; clinical predictive model; nomogram; Pediatrics; RJ1-570
• Source: Frontiers in Pediatrics, Vol 10 (2022)

ObjectivesTo explore the risk factors for renal damage in childhood immunoglobulin A vasculitis (IgAV) within 6 months and construct a clinical model for individual risk prediction.MethodsWe retrospectively analyzed the clinical data of 1,007 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.ResultsAge, 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 ~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.ConclusionThe 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.Clinical trial registration:www.chictr.org.cn, identifier ChiCTR2000033435.