Development and validation of a nomogram model for mortality prediction in stable chronic obstructive pulmonary disease patients: A prospective observational study in the RealDTC cohort

作者：Wei Cheng, Aiyuan Zhou, Qing Song, Yuqin Zeng, Ling Lin, Cong Liu, Jingcheng Shi, Zijing Zhou, Yating Peng, Jing Li, Dingding Deng, Min Yang, Lizhen Yang, Yan Chen, Shan Cai, Ping Chen · 发表于：Journal of Global Health · 年份：2024 · DOI：10.7189/jogh.14.04049 · 被引用次数：9 · 研究领域：Chronic Obstructive Pulmonary Disease (COPD) Research、Chronic Disease Management Strategies、Cardiovascular Health and Risk Factors

Background: Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. There is no nomogram model available for mortality prediction of stable COPD. We intended to develop and validate a nomogram model to predict mortality risk in stable COPD patients for personalised prognostic assessment. Methods: A prospective observational study was made of COPD outpatients registered in the RealDTC study between December 2016 and December 2019. Patients were randomly assigned to the training cohort and validation cohort in a ratio of 7:3. We used Lasso regression to screen predicted variables. Further, we evaluated the prognostic performance using the area under the time-dependent receiver operating characteristic curve (AUC) and calibration curve. We used the AUC, concordance index, and decision curve analysis to evaluate the net benefits and utility of the nomogram compared with three earlier prediction models. Results: Of 2499 patients, the median follow-up was 38 months. The characteristics of the patients between the training cohort (n = 1743) and the validation cohort (n = 756) were similar. ABEODS nomogram model, combining age, body mass index, educational level, airflow obstruction, dyspnoea, and severe exacerbation in the first year, was constructed to predict mortality in stable COPD patients. In the integrative analysis of training and validation cohorts of the nomogram model, the three-year mortality prediction achieved AUC = 0.84; 95% confide...