Gu Wen, Li Lingling, Ahmad Ashfaq, Lv Jing, Zhang Songling, Du Yajuan, Shi Jite, Ding Yiming, Liu Ting, Fan Fenling
Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
J Clin Hypertens (Greenwich). 2025 Sep;27(9):e70132. doi: 10.1111/jch.70132.
Pulmonary hypertension (PH) is a common complication in patients with chronic kidney disease (CKD) and is associated with high mortality. Early detection and proper management may improve outcomes in high-risk patients. This study aimed to develop a simple and effective model for screening PH risk in this population. We retrospectively screened 1082 CKD patients. Feature selection was performed using the least absolute shrinkage and selection operator, univariate and multivariate logistic regression (LR). Nomograms were developed for PH risk assessment. The discriminative ability was estimated by the area under the receiver operating characteristic curve (AUROC), and the accuracy was assessed with a Brier score. Models were validated externally by calculating their performance on a validation cohort. Eight machine learning models were developed, and their performance was evaluated. Decision curve analysis and clinical impact curve were used to assess the model's clinical usefulness. A total of 440 patients were included in the analysis, with 308 in the development cohort and 132 in the validation cohort. The final nomogram included five variables as follows: haemoglobin, gamma-glutamyl transferase, triglycerides, coronary heart disease and NT-proBNP. The AUROC of the model was 0.772 (95% CI: 0.731-0.806). External validation confirmed the model's good performance, with an AUROC of 0.782 (95% CI: 0.696-0.854). Among the eight machine learning models, LR showed the best performance. We developed a machine learning model based on clinical and biochemical features to assess PH risk in CKD patients. It enables early detection and risk stratification during follow-up.
肺动脉高压（PH）是慢性肾脏病（CKD）患者的常见并发症，与高死亡率相关。早期检测和适当管理可能改善高危患者的预后。本研究旨在开发一种简单有效的模型，用于筛查该人群的PH风险。我们回顾性筛查了1082例CKD患者。使用最小绝对收缩和选择算子、单变量和多变量逻辑回归（LR）进行特征选择。开发了用于PH风险评估的列线图。通过受试者操作特征曲线下面积（AUROC）估计判别能力，并用Brier评分评估准确性。通过计算模型在验证队列中的表现进行外部验证。开发了八个机器学习模型，并评估了它们的性能。使用决策曲线分析和临床影响曲线评估模型的临床实用性。共有440例患者纳入分析，其中308例在开发队列，132例在验证队列。最终的列线图包括五个变量，如下所示：血红蛋白、γ-谷氨酰转移酶、甘油三酯、冠心病和NT-proBNP。该模型的AUROC为0.772（95%CI：0.731-0.806）。外部验证证实了该模型的良好性能，AUROC为0.782（95%CI：0.696-0.854）。在八个机器学习模型中，LR表现最佳。我们开发了一种基于临床和生化特征的机器学习模型，以评估CKD患者的PH风险。它能够在随访期间进行早期检测和风险分层。
