陕西医学杂志2026，Vol.55Issue(9)：1208-1212,5.DOI:10.3969/j.issn.1000-7377.2026.09.008
慢性阻塞性肺疾病急性加重期患者继发肺动脉高压的危险因素分析及预测模型构建
Risk factors analysis and prediction model construction of secondary pulmonary hypertension in patients with AECOPD
摘要
Abstract
Objective:To explore the risk factors of secondary pulmonary hypertension(PH)in patients with acute exacerbation of chronic obstructive pulmonary disease(AECOPD),and to construct a prediction model for analysis.Methods:A total of 100 patients with AECOPD were selected.According to whether they had secondary PH,the patients were divided into an observation group(secondary PH,54 cases)and a control group(AECOPD,46 cases).The influencing factors of secondary PH were analyzed by multivariate Logistic regression.The influencing factors were selected to establish a nomogram model,and the receiver operating characteristic(ROC)curve was used to analyze the prediction of the improved model.Results:The age,neutrophil/lymphocyte ratio(NLR),brain natri-uretic peptide(BNP),and D-dimer levels in the observation group were higher than those in the control group,and the lymphocyte count and FEV1％pred were lower than those in the control group(all P＜0.05).Age,D-dimer,BNP and FEV1％pred were independent influencing factors(all P＜0.05).Based on the above influencing factors,a nomogram model was constructed.After ROC curve analysis,the area under the curve(AUC)of the nomogram model for predicting secondary PH in patients with AECOPD was 0.891(95％CI:0.802～0.950,P＜0.05),which had a good prediction efficiency.Conclusion:The independent risk factors for secondary PH in patients with AECOPD include age,D-dimer,BNP and FEV1％pred.The nomogram prediction model based on these four indicators has good predictive efficiency.关键词
慢性阻塞性肺疾病急性加重期/肺动脉高压/危险因素/预测模型/列线图模型/脑钠肽Key words
AECOPD/Pulmonary hypertension/Risk factors/Prediction model/Nomogram/Brain natriuretic peptide分类
医药卫生引用本文复制引用
孙真,王英,宋云熙,梁燕,刘复州,胡桃红..慢性阻塞性肺疾病急性加重期患者继发肺动脉高压的危险因素分析及预测模型构建[J].陕西医学杂志,2026,55(9):1208-1212,5.基金项目
国家自然科学基金青年科学基金资助项目(81902883) （81902883）
