高级检索
当前位置: 首页 > 详情页

Nomogram to predict progression from preserved ratio impaired spirometry to chronic obstructive pulmonary disease

文献详情

资源类型:
Pubmed体系:
机构: [1]Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China. [2]State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, 610041 Chengdu, Sichuan, China. [3]Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China. [4]Institute of Respiratory Health, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China. [5]The Research Units of West China, Chinese Academy of Medical Sciences, West China Hospital, Chengdu, Sichuan, China. [6]Institute of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, Sichuan, China. [7]Precision Medicine Center, Precision Medicine Key Laboratory of Sichuan Province, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
出处:

关键词: Chronic obstructive pulmonary disease (COPD) Prediction model Preserved ratio impaired spirometry (PRISm) Progression

摘要:
Preserved Ratio Impaired Spirometry (PRISm) is a specific subtype of pre-chronic obstructive pulmonary disease (pre-COPD). People with PRISm are at risk of progression to chronic obstructive pulmonary disease (COPD). We developed a model to predict progression in subjects with PRISm. We screened 188 patients whose lung function transitioned from PRISm to COPD and 173 patients with PRISm who remained stable over two years. After excluding 78 patients due to incomplete clinical or laboratory data, a total of 283 patients were included in the final analysis. These patients were randomly divided into a training cohort (227 patients) and a validation cohort (56 patients) at a 8:2 ratio. LASSO regression and multivariate logistic regression were used to identify factors influencing progression. Among the 283 patients, 134 progressed to COPD. The model developed using six variables showed good performance, with areas under the receiver operating characteristic (ROC) curves of 0.87 in the training cohort and 0.79 in the validation cohort. The model demonstrated excellent calibration and was clinically meaningful, as shown by decision curve analysis (DCA) and clinical impact curve (CIC). We developed China's first prediction model for the progression of lung function from PRISm to COPD in a real-world population. This model is conducive to early identification of high-risk groups of pulmonary function deterioration, so as to provide timely intervention and delay the occurrence and progression of the disease.© 2025. The Author(s).

基金:
语种:
PubmedID:
中科院(CAS)分区:
出版当年[2025]版:
大类 | 3 区 综合性期刊
小类 | 3 区 综合性期刊
最新[2025]版:
大类 | 3 区 综合性期刊
小类 | 3 区 综合性期刊
第一作者:
第一作者机构: [1]Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China. [4]Institute of Respiratory Health, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China. [5]The Research Units of West China, Chinese Academy of Medical Sciences, West China Hospital, Chengdu, Sichuan, China. [6]Institute of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
共同第一作者:
通讯作者:
通讯机构: [1]Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China. [3]Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China. [4]Institute of Respiratory Health, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China. [5]The Research Units of West China, Chinese Academy of Medical Sciences, West China Hospital, Chengdu, Sichuan, China. [6]Institute of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
推荐引用方式(GB/T 7714):
APA:
MLA:

资源点击量:57692 今日访问量:0 总访问量:4768 更新日期:2025-05-01 建议使用谷歌、火狐浏览器 常见问题

版权所有©2020 四川省肿瘤医院 技术支持:重庆聚合科技有限公司 地址:成都市人民南路四段55号