Department of Nephrology, West China Hospital, Sichuan University, No. 37, Guoxue alley, Chengdu, Sichuan Zipcode: 610000, China.
Department of Nephrology, West China Hospital, Sichuan University, No. 37, Guoxue alley, Chengdu, Sichuan Zipcode: 610000, China.
Int Immunopharmacol. 2021 Dec;101(Pt B):108341. doi: 10.1016/j.intimp.2021.108341. Epub 2021 Nov 11.
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.
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.
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.
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.
免疫球蛋白 A 血管炎肾损害（IgAVN）的早期缓解对其预后有重要影响。本研究旨在基于临床实验室数据，建立预测 IgAVN 患者 1 年缓解概率的多变量模型。
回顾性评估了 187 例经肾活检证实的 IgAVN 患者的数据。采用最小绝对收缩和选择算子回归分析建立多变量逻辑回归模型。基于多变量逻辑回归模型构建列线图，以便于临床应用。采用一致性指数、接收者操作特征（ROC）曲线、决策曲线分析（DCA）和临床影响曲线（CIC）评估该列线图的预测准确性和临床价值。
多变量模型中的预测因素包括病程、性别、呼吸道感染、关节炎、水肿、估计肾小球滤过率、24 小时尿蛋白、尿酸和肾脏超声强度。在训练集和测试集中，该列线图的曲线下面积（AUC）分别为 0.814 和 0.822，表明其具有良好的预测能力。此外，DCA 曲线和 CIC 显示了其临床实用性。
该多变量预测模型综合了 IgAVN 患者的临床和实验室因素，有助于对 1 年缓解概率进行个体化预测，为 IgAVN 的治疗和管理中的临床决策提供帮助。
