Cao Weiwei, Ding Bei, Zhu Kejing, Ma Li, Zhong Minghuan, Niu Yulin
School of Nursing, Guizhou Medical University, Guiyang, Guizhou, China.
Department of Organ Transplantation, Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China.
Transplant Proc. 2025 Jul-Aug;57(6):989-1000. doi: 10.1016/j.transproceed.2025.05.012. Epub 2025 Jun 24.
To develop and evaluate a risk prediction model for unplanned readmission within 1 year following kidney transplantation using Lasso-logistic regression.
Clinical data of kidney transplant recipients from the Department of Organ Transplantation at the Affiliated Hospital of Guizhou Medical University, spanning April 2017 to June 2023, were retrospectively analyzed. Initially, Lasso regression analysis was used to select predictive variables. Subsequently, logistic regression analysis was employed to construct a risk prediction model, which was presented as a nomogram. Bootstrap repeated sampling was conducted 1000 times for internal model validation. The comprehensive efficacy of the prediction model was assessed from four dimensions: discrimination, fit, calibration, and clinical benefit.
The incidence of unplanned readmission within 1-year post-transplant was 36.48%. Serum creatinine, cystatin C, albumin, serum potassium, serum magnesium, drinking history, rejection, and length of stay were the predictors of unplanned readmission within 1 year after renal transplantation. The comprehensive ability of the risk prediction model for unplanned readmission within 1 year after renal transplantation was as follows: The area under the receiver operating characteristic curve of the nomogram model was 0.715 (95% CI: 0.673-0.757). The internal validation results showed that the corrected C-index was 0.700. The positive predictive value of the model was 0.533 and the negative predictive value was 0.785. The Hosmer-Lemeshow goodness of fit test result was χ² = 4.941, P = 0.764, indicating a satisfactory fit of the model. In the calibration curve, the actual fitting curve was well-fitted to the standard curve, and the model calibration ability was acceptable. The clinical decision curve confirmed the clinical value of the model and its positive impact on actual decision-making.
The constructed model demonstrates considerable predictive value for unplanned readmission within 1 year after kidney transplantation. It serves as a valuable tool for early clinical warning, enabling healthcare professionals to formulate personalized preventive strategies based on identified risk factors.
运用套索逻辑回归开发并评估肾移植术后1年内非计划再入院的风险预测模型。
回顾性分析2017年4月至2023年6月贵州医科大学附属医院器官移植科肾移植受者的临床资料。首先，采用套索回归分析选择预测变量。随后，运用逻辑回归分析构建风险预测模型，并以列线图形式呈现。进行1000次自抽样重复抽样以进行模型内部验证。从区分度、拟合优度、校准度和临床效益四个维度评估预测模型的综合效能。
移植后1年内非计划再入院发生率为36.48%。血清肌酐、胱抑素C、白蛋白、血清钾、血清镁、饮酒史、排斥反应和住院时长是肾移植术后1年内非计划再入院的预测因素。肾移植术后1年内非计划再入院风险预测模型的综合能力如下：列线图模型的受试者操作特征曲线下面积为0.715（95%CI：0.673 - 0.757）。内部验证结果显示校正C指数为0.700。模型的阳性预测值为0.533，阴性预测值为0.785。Hosmer-Lemeshow拟合优度检验结果为χ² = 4.941，P = 0.764，表明模型拟合良好。在校准曲线中，实际拟合曲线与标准曲线拟合良好，模型校准能力可接受。临床决策曲线证实了模型的临床价值及其对实际决策的积极影响。
构建的模型对肾移植术后1年内非计划再入院具有较高的预测价值。它是早期临床预警的宝贵工具，使医护人员能够根据识别出的风险因素制定个性化的预防策略。
