State Key Laboratory of Respiratory Diseases, National Center for Respiratory Medicine, Guangdong Key Laboratory of Vascular Diseases, National Clinical Research Center for Respiratory Diseases, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
Department of Pulmonary Circulation, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Clin Transl Med. 2024 Jun;14(6):e1702. doi: 10.1002/ctm2.1702.
Patients with pulmonary hypertension (PH) and chronic obstructive pulmonary disease (COPD) have an increased risk of disease exacerbation and decreased survival. We aimed to develop and validate a non-invasive nomogram for predicting COPD associated with severe PH and a prognostic nomogram for patients with COPD and concurrent PH (COPD-PH).
This study included 535 patients with COPD-PH from six hospitals. A multivariate logistic regression analysis was used to analyse the risk factors for severe PH in patients with COPD and a multivariate Cox regression was used for the prognostic factors of COPD-PH. Performance was assessed using calibration, the area under the receiver operating characteristic curve and decision analysis curves. Kaplan-Meier curves were used for a survival analysis. The nomograms were developed as online network software.
Tricuspid regurgitation velocity, right ventricular diameter, N-terminal pro-brain natriuretic peptide (NT-proBNP), the red blood cell count, New York Heart Association functional class and sex were non-invasive independent variables of severe PH in patients with COPD. These variables were used to construct a risk assessment nomogram with good discrimination. NT-proBNP, mean pulmonary arterial pressure, partial pressure of arterial oxygen, the platelet count and albumin were independent prognostic factors for COPD-PH and were used to create a predictive nomogram of overall survival rates.
The proposed nomograms based on a large sample size of patients with COPD-PH could be used as non-invasive clinical tools to enhance the risk assessment of severe PH in patients with COPD and for the prognosis of COPD-PH. Additionally, the online network has the potential to provide artificial intelligence-assisted diagnosis and treatment.
A multicentre study with a large sample of chronic obstructive pulmonary disease (COPD) patients diagnosed with PH through right heart catheterisation. A non-invasive online clinical tool for assessing severe pulmonary hypertension (PH) in COPD. The first risk assessment tool was established for Chinese patients with COPD-PH.
患有肺动脉高压（PH）和慢性阻塞性肺疾病（COPD）的患者疾病恶化风险增加，生存率降低。我们旨在开发和验证一种用于预测 COPD 合并严重 PH 的非侵入性列线图和用于 COPD 合并 PH（COPD-PH）患者的预后列线图。
本研究纳入了来自六家医院的 535 例 COPD-PH 患者。使用多变量逻辑回归分析来分析 COPD 患者发生严重 PH 的危险因素，使用多变量 Cox 回归分析 COPD-PH 的预后因素。使用校准、接收者操作特征曲线下面积和决策分析曲线来评估性能。使用 Kaplan-Meier 曲线进行生存分析。列线图被开发为在线网络软件。
三尖瓣反流速度、右心室直径、N 末端脑利钠肽前体（NT-proBNP）、红细胞计数、纽约心脏协会功能分级和性别是非 COPD 患者严重 PH 的独立预测因素。这些变量被用于构建具有良好区分度的风险评估列线图。NT-proBNP、平均肺动脉压、动脉血氧分压、血小板计数和白蛋白是 COPD-PH 的独立预后因素，用于构建总生存率预测列线图。
基于 COPD-PH 患者大样本量的提出的列线图可以作为非侵入性临床工具，用于增强 COPD 患者严重 PH 的风险评估和 COPD-PH 的预后。此外，在线网络具有提供人工智能辅助诊断和治疗的潜力。
一项多中心研究，纳入了通过右心导管检查诊断为 PH 的 COPD 患者的大样本量。一种用于评估 COPD 中严重 PH 的非侵入性在线临床工具。为中国 COPD-PH 患者建立了首个风险评估工具。
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