Shen Lu, Miao Li, Xu Lian
Department of Pediatrics, the First People's Hospital of Lianyungang, The Affiliated Lianyungang Hospital of Xuzhou Medical University, The First Affiliated Hospital of Kangda College of Nanjing Medical University, Lianyungang Clinical College of Nanjing Medical University, Lianyungang, Jiangsu, China.
Front Pediatr. 2025 Jul 29;13:1584768. doi: 10.3389/fped.2025.1584768. eCollection 2025.
To explore the risk factors associated with renal injury in patients diagnosed with IgA vasculitis at initial presentation.
A retrospective analysis was conducted on the clinical data of 384 children who were newly diagnosed with Immunoglobulin A vasculitis (IgAV) and hospitalized between July 2020 and June 2023. The participants were categorized into two groups based on whether their 24-hour urinary protein levels exceeded 150 mg upon admission. Specifically, those with a 24 h urinary protein level exceeding 150 mg were classified as the IgA vasculitis nephritis (IgAVN), while the remaining participants were included in the IgAV group. A comparative assessment was performed to evaluate the general condition and laboratory examination results of both groups. The logistic regression analysis was utilized to pinpoint variables correlated with renal injury, facilitating the development of a risk prediction model. The receiver operating characteristic curve(ROC) was employed to evaluate the model's predictive performance.
The univariate analysis revealed that the duration of rash, gender, patient age, levels of C-reactive protein(CRP), Immunoglobulin G (IgG), albumin, globulin, glutamic oxaloacetic transaminase(AST), total cholesterol(TC), urine routine results, and 25-(OH)-D were all identified as potential influencing factors for IgAVN. Multivariate analysis revealed that albumin, patient age, and TC emerged as independent influential factors in the occurrence of IgAVN. The area under the curve (AUC) for the combined predictor(age + albumin + TC) was significantly larger than that of individual factors such as age, albumin, and TC, with respective AUC values of 0.804, 0.673, 0.737, and 0.608. The prediction model of IgAVN was further developed as follows: logit(P) = 3.978 + 0.199 × age (years) - 0.197 × albumin (g/L) + 0.550 × TC (mmol/L).
By utilizing certain laboratory indicators, it is possible to enhance the prediction of IgAVN, thereby reinforcing the importance of follow-up measures for early detection, diagnosis, and treatment in order to mitigate the occurrence of unfavorable prognosis.
探讨初诊为IgA血管炎患者发生肾损伤的相关危险因素。
对2020年7月至2023年6月期间新诊断为免疫球蛋白A血管炎（IgAV）并住院的384例儿童的临床资料进行回顾性分析。根据入院时24小时尿蛋白水平是否超过150mg将参与者分为两组。具体而言，24小时尿蛋白水平超过150mg者被归类为IgA血管炎肾病（IgAVN），其余参与者纳入IgAV组。对两组的一般情况和实验室检查结果进行比较评估。采用逻辑回归分析确定与肾损伤相关的变量，以建立风险预测模型。采用受试者工作特征曲线（ROC）评估模型的预测性能。
单因素分析显示，皮疹持续时间、性别、患者年龄、C反应蛋白（CRP）、免疫球蛋白G（IgG）、白蛋白、球蛋白、谷草转氨酶（AST）、总胆固醇（TC）水平、尿常规结果和25-（OH）-D均被确定为IgAVN的潜在影响因素。多因素分析显示，白蛋白、患者年龄和TC是IgAVN发生的独立影响因素。联合预测指标（年龄+白蛋白+TC）的曲线下面积（AUC）显著大于年龄、白蛋白和TC等单个因素，其AUC值分别为0.804、0.673、0.737和0.608。IgAVN的预测模型进一步构建如下：logit（P）=3.978+0.199×年龄（岁）-0.197×白蛋白（g/L）+0.550×TC（mmol/L）。
利用某些实验室指标可提高对IgAVN的预测，从而强化随访措施对于早期发现、诊断和治疗的重要性，以减少不良预后的发生。
