Zhang Long, Liu Yiting, Zou Jilin, Wang Tianyu, Hu Haochong, Zhou Yujie, Lu Yifan, Qiu Tao, Zhou Jiangqiao, Liu Xiuheng
Department of Organ Transplantation, Renmin Hospital of Wuhan University, Wuhan 430060, China.
Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China.
Biomedicines. 2024 Feb 4;12(2):366. doi: 10.3390/biomedicines12020366.
This study aimed to develop a simple predictive model for early identification of the risk of adverse outcomes in kidney transplant-associated pneumonia (PCP) patients.
This study encompassed 103 patients diagnosed with PCP, who received treatment at our hospital between 2018 and 2023. Among these participants, 20 were categorized as suffering from severe PCP, and, regrettably, 13 among them succumbed. Through the application of machine learning techniques and multivariate logistic regression analysis, two pivotal variables were discerned and subsequently integrated into a nomogram. The efficacy of the model was assessed via receiver operating characteristic (ROC) curves and calibration curves. Additionally, decision curve analysis (DCA) and a clinical impact curve (CIC) were employed to evaluate the clinical utility of the model. The Kaplan-Meier (KM) survival curves were utilized to ascertain the model's aptitude for risk stratification.
Hematological markers, namely Procalcitonin (PCT) and C-reactive protein (CRP)-to-albumin ratio (CAR), were identified through machine learning and multivariate logistic regression. These variables were subsequently utilized to formulate a predictive model, presented in the form of a nomogram. The ROC curve exhibited commendable predictive accuracy in both internal validation (AUC = 0.861) and external validation (AUC = 0.896). Within a specific threshold probability range, both DCA and CIC demonstrated notable performance. Moreover, the KM survival curve further substantiated the nomogram's efficacy in risk stratification.
Based on hematological parameters, especially CAR and PCT, a simple nomogram was established to stratify prognostic risk in patients with renal transplant-related PCP.
本研究旨在开发一种简单的预测模型，用于早期识别肾移植相关肺炎（PCP）患者不良结局的风险。
本研究纳入了103例确诊为PCP的患者，这些患者于2018年至2023年在我院接受治疗。在这些参与者中，20例被归类为患有严重PCP，遗憾的是，其中13例死亡。通过应用机器学习技术和多因素逻辑回归分析，识别出两个关键变量，随后将其整合到列线图中。通过受试者工作特征（ROC）曲线和校准曲线评估模型的有效性。此外，采用决策曲线分析（DCA）和临床影响曲线（CIC）来评估模型的临床实用性。利用Kaplan-Meier（KM）生存曲线来确定模型进行风险分层的能力。
通过机器学习和多因素逻辑回归确定了血液学标志物，即降钙素原（PCT）和C反应蛋白（CRP）与白蛋白比值（CAR）。这些变量随后被用于构建以列线图形式呈现的预测模型。ROC曲线在内部验证（AUC = 0.861）和外部验证（AUC = 0.896）中均表现出良好的预测准确性。在特定的阈值概率范围内，DCA和CIC均表现出显著性能。此外，KM生存曲线进一步证实了列线图在风险分层中的有效性。
基于血液学参数，尤其是CAR和PCT，建立了一个简单的列线图，用于对肾移植相关PCP患者的预后风险进行分层。
