Risk factors and establishment of a nomogram model for pulmonary arterial hypertension complicated by acute exacerbation of chronic obstructive pulmonary disease.

IF 1.7 4区 医学 Q3 MEDICINE, RESEARCH & EXPERIMENTAL
American journal of translational research Pub Date : 2025-05-15 eCollection Date: 2025-01-01 DOI:10.62347/XMTE6690
Haifeng Ye, Yaner Gu
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引用次数: 0

Abstract

Objective: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

肺动脉高压合并慢性阻塞性肺疾病急性加重的危险因素及nomogram模型的建立
目的:探讨慢性阻塞性肺疾病急性加重期(AECOPD)患者肺动脉高压(PAH)的危险因素,建立nomogram模型,以便临床早期识别高危患者,指导个性化治疗方案。方法:回顾性研究2018年6月至2023年5月舟山妇幼医院收治的602例AECOPD患者。根据是否存在PAH将患者分为两组。采用单因素和多因素logistic回归分析确定AECOPD合并PAH的独立危险因素。然后基于这些因素建立了nomogram模型。采用Bootstrap自采样方法对模型的预测性能进行了评价。计算受试者工作特征曲线下面积(AUC)、灵敏度、特异性和一致性指数(C-index)等指标,评价模型的判别性和校准性。结果:602例AECOPD患者中,发生PAH的占8.31%。与非PAH组相比,PAH组表现出更高比例的慢性阻塞性肺疾病(GOLD) IV级,高血压,中性粒细胞与淋巴细胞比率(NLR),血小板与淋巴细胞比率(PLR)和n端前脑利钠肽(NT-proBNP)水平升高。多因素logistic回归分析发现,GOLD分级、高血压、NLR、PLR和NT-proBNP是aecopd相关PAH的独立危险因素。基于这些变量建立了一种nomogram预测模型。模型在训练集中的AUC、灵敏度和特异性分别为0.906(95%置信区间(CI) 0.847 ~ 0.966)、0.850和0.862,在验证集中的AUC、灵敏度和特异性分别为0.861(95%置信区间(CI) 0.715 ~ 0.932)、0.700和0.948。模型在训练集和验证集的校正曲线c指数均较高,分别为0.906和0.861。决策曲线分析表明,在一定的风险阈值内,净效益为正。结论:AECOPD患者PAH与GOLD分级、高血压、NLR、PLR和NT-proBNP相关。开发的nomogram显示出强大的预测性能和临床实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
American journal of translational research
American journal of translational research ONCOLOGY-MEDICINE, RESEARCH & EXPERIMENTAL
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552
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