Zhang Fan, Shan Ying, Jian Xinyao, Qi Miao, Wei Yanling, Guo Jialong, Hou Shuang, Shi Jianqing, Xiong Zibo, Huang Xiaoyan
Department of Nephrology, Peking University Shenzhen Hospital, Shenzhen, Guangdong, 518036, China.
Clinical Research Academy, Peking University Shenzhen Hospital, Shenzhen, Guangdong, 518036, China.
Clin Rheumatol. 2025 Feb;44(2):693-700. doi: 10.1007/s10067-024-07268-w. Epub 2024 Dec 20.
To develop and validate prediction models for estimating the activity index (AI) of kidney histopathology in lupus nephritis (LN) using clinical and laboratory data.
This study used single-center data from patients with kidney biopsy-confirmed LN between January 2012 and March 2022. The development and validation datasets were temporally cut. We discriminated AI > 10 and ≤ 10 as high and mild/moderate activity status, respectively. We constructed four models for AI: Model 1 included demographic information; Model 2 additionally incorporated data of systemic conditions; Model 3 further included kidney-specific conditions; and Model 4 included all the aforementioned predictors. Logistic regression was employed in Models 1 to 3, while Model 4 utilized least absolute shrinkage and selection operator for predictor selection and model building. Internal validation was performed using 1000 bootstrap resampling, while external validation was performed in the temporal validation dataset. Both calibration and discrimination metrics were evaluated.
There were 160 patients in the development dataset and 70 patients in the validation dataset. In the temporal validation, all the models achieved acceptable calibration and excellent discrimination. Model 2 which contained relatively fewer predictors achieved the highest area under the receiver operator characteristic curve of 0.86 (95% confidence interval 0.76 to 0.94).
Our Model 2 incorporating demographic and systemic indicators exhibited good performance in estimating the AI of LN. We thus provide a simple yet effective algorithm to predict AI in patients with LN, potentially aiding clinicians in non-invasively assessing disease activity and guiding treatment decisions. Key Points • We developed a prediction model (Model 2) incorporating demographic and systemic indicators to predict AI in patients with LN. • The prediction model can aid clinicians in noninvasively assessing disease activity and guiding treatment decisions.
利用临床和实验室数据开发并验证用于估计狼疮性肾炎（LN）肾脏组织病理学活动指数（AI）的预测模型。
本研究使用了2012年1月至2022年3月期间经肾脏活检确诊为LN的患者的单中心数据。开发数据集和验证数据集按时间划分。我们将AI > 10和≤ 10分别判别为高活动状态和轻/中度活动状态。我们构建了四个用于AI的模型：模型1包括人口统计学信息；模型2额外纳入了全身状况数据；模型3进一步纳入了肾脏特异性状况；模型4包括所有上述预测因素。模型1至3采用逻辑回归，而模型4使用最小绝对收缩和选择算子进行预测因素选择和模型构建。内部验证采用1000次自助重采样，外部验证在时间验证数据集中进行。评估了校准和区分指标。
开发数据集中有160例患者，验证数据集中有70例患者。在时间验证中，所有模型均实现了可接受的校准和出色的区分能力。包含相对较少预测因素的模型2在接受者操作特征曲线下的面积最高，为0.86（95%置信区间0.76至0.94）。
我们纳入人口统计学和全身指标的模型2在估计LN的AI方面表现良好。因此，我们提供了一种简单而有效的算法来预测LN患者的AI，可能有助于临床医生无创评估疾病活动并指导治疗决策。要点 • 我们开发了一个纳入人口统计学和全身指标的预测模型（模型2）来预测LN患者的AI。 • 该预测模型可帮助临床医生无创评估疾病活动并指导治疗决策。
