Yao Yi, Astor Brad C, Yang Wei, Greene Tom, Li Liang
Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA.
BMC Med Res Methodol. 2024 Dec 31;24(1):324. doi: 10.1186/s12874-024-02445-6.
Graft loss is a major health concern for kidney transplant (KTx) recipients. It is of clinical interest to develop a prognostic model for both graft function, quantified by estimated glomerular filtration rate (eGFR), and the risk of graft failure. Additionally, the model should be dynamic in the sense that it adapts to accumulating longitudinal information, including time-varying at-risk population, predictor-outcome association, and clinical history. Finally, the model should also properly account for the competing risk by death with a functioning graft. A model with the features above is not yet available in the literature and is the focus of this research.
We built and internally validated a prediction model on 3,893 patients from the Wisconsin Allograft Recipient Database (WisARD) who had a functioning graft 6 months after kidney transplantation. The landmark analysis approach was used to build a proof-of-concept dynamic prediction model to address the aforementioned methodological issues: the prediction of graft failure, accounted for competing risk of death, as well as the future eGFR value, are updated at each post-transplant time. We used 21 predictors including recipient characteristics, donor characteristics, transplant-related and post-transplant factors, longitudinal eGFR, hospitalization, and rejection history. A sensitivity analysis explored a less conservative variable selection rule that resulted in a more parsimonious model with reduced predictors.
For prediction up to the next 1 to 5 years, the model achieved high accuracy in predicting graft failure, with the AUC between 0.80 and 0.95, and moderately high accuracy in predicting eGFR, with the root mean squared error between 10 and 18 mL/min/1.73m2 and 70%-90% of predicted eGFR falling within 30% of the observed eGFR. The model demonstrated substantial accuracy improvement compared to a conventional prediction model that used only baseline predictors.
The model outperformed conventional prediction model that used only baseline predictors. It is a useful tool for patient counseling and clinical management of KTx and is currently available as a web app.
移植物丢失是肾移植（KTx）受者主要的健康问题。开发一个针对移植物功能（通过估算肾小球滤过率（eGFR）量化）和移植物失功风险的预后模型具有临床意义。此外，该模型应具有动态性，即它能适应不断积累的纵向信息，包括随时间变化的风险人群、预测因素与结果的关联以及临床病史。最后，该模型还应妥善考虑有功能移植物情况下死亡这一竞争风险。目前文献中尚无具备上述特征的模型，这也是本研究的重点。
我们基于威斯康星同种异体移植受者数据库（WisARD）中的3893例患者构建并进行了内部验证，这些患者在肾移植后6个月时移植物功能良好。采用标志性分析方法构建一个概念验证性动态预测模型，以解决上述方法学问题：移植物失功的预测、考虑死亡的竞争风险以及未来eGFR值，在每次移植后时间点都会更新。我们使用了21个预测因素，包括受者特征、供者特征、移植相关和移植后因素、纵向eGFR、住院情况以及排斥反应史。敏感性分析探索了一种不太保守的变量选择规则，从而得到一个预测因素更少、更为简洁的模型。
对于未来1至5年的预测，该模型在预测移植物失功方面具有较高的准确性，曲线下面积（AUC）在0.80至0.95之间，在预测eGFR方面具有中等偏高的准确性，均方根误差在10至18 mL/min/1.73m²之间，且70% - 90%的预测eGFR值落在观察到的eGFR值的30%范围内。与仅使用基线预测因素的传统预测模型相比，该模型的准确性有显著提高。
该模型优于仅使用基线预测因素的传统预测模型。它是肾移植患者咨询和临床管理的有用工具，目前可作为网络应用程序使用。
