实用医学杂志2026，Vol.42Issue(16)：2918-2925,8.DOI:10.3969/j.issn.1006-5725.2026.16.007
慢性阻塞性肺疾病急性加重风险预测研究进展:从宏观临床评分到多模态融合与临床转化
Advances in predicting the risk of acute exacerbations in COPD:From macroscopic clinical scores to mul-timodal integration and clinical translation
摘要
Abstract
This review systematically reviews existing research on risk prediction for acute exacerbations of chronic obstructive pulmonary disease(AECOPD),clarifies the inherent evolutionary logic of relevant research paradigms transitioning from macroscopic clinical scoring systems to microscopic biomarkers,and elaborates on the trade-offs between prediction accuracy,clinical accessibility and methodological quality across different prediction strategies.Based on comprehensive literature retrieval,this paper systematically sorts out and summarizes AECOPD risk prediction-related research,covering prognostic scoring systems,inflammatory immune markers,radiomics,and machine learning-based methods.Among multidimensional scoring systems,the DOSE index is primarily used to predict the risk of future acute exacerbations and the frequency of hospitalization,and it is positively correlated with the number of outpatient visits and hospitalization costs(all P<0.001).The ADO index is mainly applied to predict 1-3 year all-cause mortality,with a C-statistic of 0.730 for 1-year mortality in external validation.Its pre-dictive performance is further improved after integrating the ADO index with anemia,nutritional status and other indicators.The BODE index achieves an AUC of 0.82 for predicting 3-year mortality,but its high dependence on cardiopulmonary exercise testing limits its popularization in primary medical institutions.In terms of inflammatory and immune markers,the combined AUC of SII,SIRI,NLR,PLR and MLR for the diagnosis of moderate-to-severe AECOPD reaches 0.837,and the nomogram constructed based on these predictors yields an AUC of 0.806-0.839 for predicting AECOPD-related respiratory failure.The combined model of radiomics and machine learning achieves an AUC of up to 0.957 for AECOPD identification,but existing systematic reviews show that the pooled AUC of existing AI/ML models only ranges from 0.73 to 0.77,and 57.9%of included studies are assessed as having a high risk of bias.The BLISS score is designed to predict the risk of respiratory-related hospitalization within the next two years,while the PRECISE-X model predicts the first severe acute exacerbation,enabling a prospective paradigm shift from"post-event assessment"to"diagnosis-based risk stratification".AECOPD risk prediction is developing towards a multi-modal,dynamic and early-stage direction.The core priority for future research is not to develop more complex individual models,but to construct a hierarchical and progressive validation system:estab-lishing unified testing standards for core indicators,implementing rigorous multi-center external validation,and integrating machine learning methods on the premise of ensuring model interpretability.关键词
慢性阻塞性肺疾病急性加重/预测模型/炎症标志物/影像学/机器学习Key words
acute exacerbation of chronic obstructive pulmonary disease/predictive models/inflam-matory biomarker/imaging/machine learning分类
医药卫生引用本文复制引用
尤奕铭,江丽,罗彩虹,邹展声,余常辉,蔡绍曦..慢性阻塞性肺疾病急性加重风险预测研究进展:从宏观临床评分到多模态融合与临床转化[J].实用医学杂志,2026,42(16):2918-2925,8.基金项目
国家自然科学基金项目(编号:82270024) （编号:82270024）
广东省基础与应用基础研究基金项目(编号:2023A1515030246) （编号:2023A1515030246）
