Predicting the frequent exacerbator phenotype in COPD: development and validation of a multicenter real-world prediction model

作者：Hongbing Peng, Yiming Zhou, Shuaiji Lu, Ying Nie, Jian‐Ting Zhang, Jijun Yang · 发表于：BMC Medical Informatics and Decision Making · 年份：2025 · DOI：10.1186/s12911-025-03281-4 · 研究领域：Chronic Obstructive Pulmonary Disease (COPD) Research、Chronic Disease Management Strategies、Respiratory and Cough-Related Research

BACKGROUND: The frequent exacerbator phenotype (FEP) of chronic obstructive pulmonary disease (COPD) significantly impacts quality of life, increases healthcare burden, and increases mortality rates. This study aims to develop an interpretable machine learning model for the early prediction of FEP to improve patient prognosis. METHODS: Retrospective data were collected from the electronic health records (EHRs) of two hospitals for three independent cohorts of patients hospitalized for the first time due to acute exacerbation of COPD (AECOPD). Patients were categorized into frequent exacerbation and nonfrequent exacerbation groups on the basis of whether they experienced two or more exacerbations requiring hospitalization during a 12-month follow-up period. The feature variables were selected via univariate regression combined with the Boruta algorithm. Nine machine learning models were developed and validated via 5-fold cross-validation. The optimal prediction model was selected by integrating performance on the test set, two independent external datasets, and clinical requirements. The global and local interpretability of the model was achieved via Shapley additive explanations (SHAPs). Restricted cubic splines (RCSs) were employed to analyze the dose‒response relationships between continuous variables and the frequent exacerbator phenotype. Ultimately, the model was deployed on the Shiny platform. RESULTS: This study included a development cohort of 1,310 patients and two e...