Du Wenwen, Wang Xiaoxing, Zhang Dan, Chen Wenqian, Zuo Xianbo, Li Pengmei
Department of Pharmacy, Friendship Hospital, Chaoyang District, Beijing, 100029, China.
Department of Dermatology, Department of Pharmacy, Friendship Hospital, Beijing, Chaoyang District, 100029, China.
BMC Nephrol. 2024 Dec 18;25(1):458. doi: 10.1186/s12882-024-03871-w.
This study aimed to develop a nomogram for predicting persistent renal dysfunction in acute kidney injury (AKI) following lung transplantation (LTx).
A total of 229 LTx patients were enrolled, and genotyping for 153 single nucleotide polymorphisms (SNPs) was performed. The cohort was randomly divided into training (n = 183) and validation (n = 46) sets in an 8:2 ratio. Statistically significant SNPs identified through pharmacogenomic analysis were combined with clinical factors to construct a comprehensive prediction model for persistent AKI using multivariate logistic regression analysis. Discrimination and calibration analyses were conducted to evaluate the performance of the model. Decision curve analysis was used to assess its clinical utility. Due to the small sample size, bootstrap internal sampling with 500 iterations was adopted for validation to prevent overfitting of the model.
The final nomogram comprised nine predictors, including body mass index, thrombin time, tacrolimus initial concentration, rs757210, rs1799884, rs6887695, rs1494558, rs2069762 and rs2275913. In the training set, the area under the receiver operating characteristic curve of the nomogram was 0.781 (95%CI: 0.715-0.846), while in the validation set it was 0.698 (95%CI: 0.542-0.855), indicating good model fit. As demonstrated by 500 Bootstrap internal sampling validations, the model has high discrimination and calibration. Additionally, decision curve analysis confirmed its clinical applicability.
This study presents a genotype-guided nomogram that can be used to assess the risk of persistent AKI following LTx and may assist in guiding personalized prevention strategies in clinical practice.
本研究旨在开发一种列线图，用于预测肺移植（LTx）后急性肾损伤（AKI）患者持续性肾功能不全的发生风险。
共纳入229例LTx患者，并对153个单核苷酸多态性（SNP）进行基因分型。按照8:2的比例将队列随机分为训练集（n = 183）和验证集（n = 46）。通过药物基因组学分析确定的具有统计学意义的SNP与临床因素相结合，采用多因素逻辑回归分析构建持续性AKI的综合预测模型。进行判别分析和校准分析以评估模型的性能。使用决策曲线分析评估其临床实用性。由于样本量较小，采用500次迭代的自助法内部抽样进行验证，以防止模型过度拟合。
最终的列线图包含9个预测因子，包括体重指数、凝血酶时间、他克莫司初始浓度、rs757210、rs1799884、rs6887695、rs1494558、rs2069762和rs2275913。在训练集中，列线图的受试者工作特征曲线下面积为0.781（95%CI：0.715 - 0.846），而在验证集中为0.698（95%CI：0.542 - 0.855），表明模型拟合良好。500次自助法内部抽样验证表明，该模型具有较高的判别力和校准度。此外，决策曲线分析证实了其临床适用性。
本研究提出了一种基于基因型的列线图，可用于评估LTx后持续性AKI的风险，并可能有助于指导临床实践中的个性化预防策略。
