Ye Haifeng, Gu Yaner
Respiratory Medicine Department, Zhoushan Women and Children's Hospital Zhoushan 316000, Zhejiang, China.
General Department, Zhoushan Women and Children's Hospital Zhoushan 316000, Zhejiang, China.
Am J Transl Res. 2025 May 15;17(5):3917-3927. doi: 10.62347/XMTE6690. eCollection 2025.
To identify risk factors for pulmonary arterial hypertension (PAH) in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) and develop a nomogram model to facilitate early clinical identification of high-risk patients and guide personalized treatment plans.
This retrospective study included 602 AECOPD patients treated at Zhoushan Women and Children's Hospital from June 2018 to May 2023. Patients were divided into two groups based on the presence or absence of PAH. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for AECOPD with PAH. A nomogram model was then established based on these factors. The Bootstrap self-sampling method was used to evaluate the predictive performance of the model. Indicators such as the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and consistency index (C-index) were calculated to evaluate the discrimination and calibration of the model.
Among 602 AECOPD patients, 8.31% developed PAH. Compared with the non-PAH group, the PAH group exhibited a higher proportion of Chronic Obstructive Lung Disease (GOLD) grade IV, hypertension, and elevated neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and N-terminal pro-brain natriuretic peptide (NT-proBNP) levels. Multivariate logistic regression analysis identified GOLD grade, hypertension, NLR, PLR, and NT-proBNP as independent risk factors for AECOPD-associated PAH. A nomogram prediction model was developed based on these variables. The model's AUC, sensitivity, and specificity in the training set were 0.906 (95% confidence interval (CI): 0.847-0.966), 0.850, and 0.862, respectively, and those in the validation set were 0.861 (95% CI: 0.715-0.932), 0.700, and 0.948, respectively. The C-index for the calibration curves of the model in both the training and validation sets was high (0.906 and 0.861, respectively). Decision curve analysis indicated a positive net benefit within a certain risk threshold.
PAH in AECOPD patients was associated with GOLD grade, hypertension, NLR, PLR, and NT-proBNP. The developed nomogram demonstrated strong predictive performance and clinical utility.
确定慢性阻塞性肺疾病急性加重期（AECOPD）患者发生肺动脉高压（PAH）的危险因素，并建立列线图模型，以促进临床早期识别高危患者并指导个性化治疗方案。
这项回顾性研究纳入了2018年6月至2023年5月在舟山市妇女儿童医院接受治疗的602例AECOPD患者。根据是否存在PAH将患者分为两组。进行单因素和多因素逻辑回归分析，以确定AECOPD合并PAH的独立危险因素。然后基于这些因素建立列线图模型。采用Bootstrap自抽样法评估模型的预测性能。计算受试者操作特征曲线下面积（AUC）、敏感性、特异性和一致性指数（C指数）等指标，以评估模型的区分度和校准度。
在602例AECOPD患者中，8.31%发生了PAH。与非PAH组相比，PAH组慢性阻塞性肺疾病（GOLD）IV级、高血压以及中性粒细胞与淋巴细胞比值（NLR）、血小板与淋巴细胞比值（PLR）和N末端脑钠肽前体（NT-proBNP）水平升高的比例更高。多因素逻辑回归分析确定GOLD分级、高血压、NLR、PLR和NT-proBNP为AECOPD相关PAH的独立危险因素。基于这些变量建立了列线图预测模型。该模型在训练集中的AUC、敏感性和特异性分别为0.906（95%置信区间（CI）：0.847-0.966）、0.850和0.862，在验证集中分别为0.861（95%CI：0.715-0.932）、0.700和0.948。模型在训练集和验证集校准曲线的C指数均较高（分别为0.906和0.861）。决策曲线分析表明在一定风险阈值内净效益为正。
AECOPD患者的PAH与GOLD分级、高血压、NLR、PLR和NT-proBNP相关。所建立的列线图显示出强大的预测性能和临床实用性。
