Wang Huifang, Chen Qiaoling, Ye Qiuping, Liu Lifang, Wei Lixin
Department of Nephrology, Fujian Medical University Union Hospital, Fuzhou, China.
Department of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Rheumatology (Oxford). 2025 May 1;64(5):2647-2655. doi: 10.1093/rheumatology/keae509.
Interstitial fibrosis and tubular atrophy (IFTA) were frequent histologic features of LN, and LN patients with IFTA have poor renal outcomes. In this study, we aimed to construct prediction models for the IFTA in LN patients.
This retrospective study included 303 patients with biopsy-proven LN at the Affiliated Hospital of Qingdao University and Fujian Medical University Union Hospital. The participants were randomly divided into development and validation cohorts. They were further divided into IFTA and non-IFTA groups. The least absolute shrinkage and selection operator (LASSO) regression model with laboratory test results collected at the time of kidney biopsy was used to optimize feature selection for the risk model. Multivariable logistic regression analysis was applied to build a predicting model incorporating the feature selected in the LASSO regression model. Discrimination, calibration, and clinical usefulness of the predicting model were assessed using the C-index, calibration plot, and receiver operating characteristic curve analysis. Internal validation was assessed using the bootstrapping validation. A nomogram for individual assessment was constructed based on the preferable model.
Predictors contained in the prediction nomogram included age, BMI, mean arterial pressure, log antinuclear antibody (logANA), C3, estimated glomerular filtration rate and serum uric acid. The model displayed good discrimination with a C-index of 0.794 (95% CI 0.734-0.854) and good calibration. High C-index value of 0.857 (95% CI 0.776-0.938) could still be reached in the interval validation. A nomogram model based on the LASSO model was created for producing a probability score of IFTA in LN patients.
With excellent predictive abilities, the nomogram may provide a simple and reliable tool to distinguish LN patients with IFTA and help physicians make clinical decisions in their comprehensive assessment.
间质纤维化和肾小管萎缩（IFTA）是狼疮性肾炎（LN）常见的组织学特征，伴有IFTA的LN患者肾脏预后较差。在本研究中，我们旨在构建LN患者IFTA的预测模型。
这项回顾性研究纳入了青岛大学附属医院和福建医科大学附属协和医院303例经活检证实为LN的患者。参与者被随机分为开发队列和验证队列。他们又进一步分为IFTA组和非IFTA组。使用在肾活检时收集的实验室检测结果的最小绝对收缩和选择算子（LASSO）回归模型来优化风险模型的特征选择。应用多变量逻辑回归分析建立一个包含LASSO回归模型中所选特征的预测模型。使用C指数、校准图和受试者操作特征曲线分析来评估预测模型的辨别力、校准度和临床实用性。使用自助验证评估内部验证。基于优选模型构建个体评估的列线图。
预测列线图中的预测因素包括年龄、体重指数、平均动脉压、抗核抗体对数（logANA）、C3、估计肾小球滤过率和血清尿酸。该模型显示出良好的辨别力，C指数为0.794（95%CI 0.734 - 0.854），校准度良好。在区间验证中仍可达到0.857（95%CI 从0.776至0.938）的高C指数值。基于LASSO模型创建了一个列线图模型，用于生成LN患者发生IFTA的概率评分。
该列线图具有出色预测能力，可为区分伴有IFTA的LN患者提供一个简单可靠的工具，并帮助医生在综合评估中做出临床决策。
