Fu Ruqian, Yang Manqiong, Li Zhihui, Kang Zhijuan, Xun Mai, Wang Ying, Wang Manzhi, Wang Xiangyun
Academy of Pediatrics of University of South China, Changsha, China.
Department of Nephrology and Rheumatology of Hunan Children's Hospital, Changsha, China.
Front Pediatr. 2022 Aug 17;10:967249. doi: 10.3389/fped.2022.967249. eCollection 2022.
To 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.
We 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.
Age, persistent cutaneous purpura, erythrocyte distribution width, complement C, 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, > 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 2584% and 14~73%, respectively.
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.
www.chictr.org.cn, identifier ChiCTR2000033435.
探讨儿童免疫球蛋白A血管炎（IgAV）6个月内肾损伤的危险因素，并构建个体风险预测的临床模型。
回顾性分析我院1007例及其他医院287例诊断为IgAV的儿童临床资料。我院约70%的病例使用统计产品与服务解决方案（SPSS）软件随机选取用于建模。其余30%的病例用于内部验证，其他医院的病例用于外部验证。通过单因素和多因素逻辑回归分析对建模数据进行分析，构建IgAV患儿肾损伤的临床预测模型。然后，评估并验证模型的区分度、校准度和临床实用性。最后，以列线图的形式呈现预测模型。
年龄、持续性皮肤紫癜、红细胞分布宽度、补体C、免疫球蛋白G和甘油三酯是IgAV肾损伤的独立影响因素。基于这些因素，预测模型的曲线下面积（AUC）为0.772；校准曲线与理想曲线无显著偏差；当预测概率约为15 - 82%时，临床决策曲线高于两条极端线。将内部和外部验证数据集应用于预测模型时，AUC分别为0.729和0.750，Z检验与建模AUC比较，>0.05。校准曲线在理想曲线周围波动，当预测概率分别为25% - 84%和14% - 73%时，临床决策曲线高于两条极端线。
该预测模型具有良好的区分度、校准度和临床实用性。内部或外部验证均具有较好的临床疗效，表明该模型具有可重复性和可移植性。
