Development and Validation of a Machine Learning Model for Predicting Three-Month Unplanned Readmission in Patients with Moderate-to-Severe Chronic Obstructive Pulmonary Disease: A Retrospective Cohort Study
- 1Department of Respiratory and Critical Care Medicine, Chest Hospital, Tianjin University, Tianjin, People’s Republic of China
Abstract
Purpose: Patients with moderate-to-severe chronic obstructive pulmonary disease (COPD) face a high risk of three-month unplanned readmission after discharge, yet early identification of high-risk patients remains challenging in clinical practice. This study aimed to develop and validate machine learning (ML) models for predicting the risk and providing a reference for the subsequent development and updating of predictive tools.
Methods: This single-center retrospective cohort study involved individuals with moderate-to-severe COPD from Tianjin Chest Hospital (January 2023 to January 2026). Participants were randomly split into a 70% training set and a 30% validation set. Least absolute shrinkage and selection operator (LASSO) regression identified candidate predictors for model development. Six models were constructed: logistic regression (LR), support vector machine (SVM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Backpropagation Neural Network (BPNN), and Naive Bayes (NB). Model performance was assessed through the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity (SEN), and specificity (SPE). Calibration was evaluated through calibration curves, and clinical utility via decision curve analysis (DCA).
Results: 299 patients with moderate-to-severe COPD were enrolled, and 59 (20%) experienced unplanned readmission within three months after discharge. Six ML models were compared, and the XGBoost model exhibited the best performance in the validation cohort, with an AUC of 0.809 (95% CI: 0.690–0.927), SEN of 0.750, and SPE of 0.826. SHapley Additive exPlanations (SHAP) analysis ranked the key predictive factors in descending order: history of hospitalization in the past year, FEV1, history of coronary heart disease, and smoking history.
Conclusion: XGBoost model showed promising performance in internal validation for predicting the risk of unplanned readmission among patients with moderate-to-severe COPD. However, these findings should be interpreted cautiously given the small sample size, single-center design. Future studies should use prospective, multicenter data from diverse geographic regions to externally validate and update the model.
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BibTeX
@article{yu2026development,
title = {{Development and Validation of a Machine Learning Model for Predicting Three-Month Unplanned Readmission in Patients with Moderate-to-Severe Chronic Obstructive Pulmonary Disease: A Retrospective Cohort Study}},
author = {Shuyu Yu and Haocheng Wei and Qiang Chen and Na Li and Zhaoxu Jiang and Hui Ma},
journal = {International Journal of Chronic Obstructive Pulmonary Disease},
year = {2026},
month = oct,
doi = {10.2147/copd.s643657},
url = {https://doi.org/10.2147/copd.s643657},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - Development and Validation of a Machine Learning Model for Predicting Three-Month Unplanned Readmission in Patients with Moderate-to-Severe Chronic Obstructive Pulmonary Disease: A Retrospective Cohort Study
AU - Shuyu Yu
AU - Haocheng Wei
AU - Qiang Chen
AU - Na Li
AU - Zhaoxu Jiang
AU - Hui Ma
PY - 2026
DA - 2026/10/27
JO - International Journal of Chronic Obstructive Pulmonary Disease
DO - 10.2147/copd.s643657
UR - https://doi.org/10.2147/copd.s643657
AB - Purpose: Patients with moderate-to-severe chronic obstructive pulmonary disease (COPD) face a high risk of three-month unplanned readmission after discharge, yet early identification of high-risk patients remains challenging in clinical practice. This study aimed to develop and validate machine learning (ML) models for predicting the risk and providing a reference for the subsequent development and updating of predictive tools. Methods: This single-center retrospective cohort study involved individuals with moderate-to-severe COPD from Tianjin Chest Hospital (January 2023 to January 2026). Participants were randomly split into a 70% training set and a 30% validation set. Least absolute shrinkage and selection operator (LASSO) regression identified candidate predictors for model development. Six models were constructed: logistic regression (LR), support vector machine (SVM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Backpropagation Neural Network (BPNN), and Naive Bayes (NB). Model performance was assessed through the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity (SEN), and specificity (SPE). Calibration was evaluated through calibration curves, and clinical utility via decision curve analysis (DCA). Results: 299 patients with moderate-to-severe COPD were enrolled, and 59 (20%) experienced unplanned readmission within three months after discharge. Six ML models were compared, and the XGBoost model exhibited the best performance in the validation cohort, with an AUC of 0.809 (95% CI: 0.690–0.927), SEN of 0.750, and SPE of 0.826. SHapley Additive exPlanations (SHAP) analysis ranked the key predictive factors in descending order: history of hospitalization in the past year, FEV1, history of coronary heart disease, and smoking history. Conclusion: XGBoost model showed promising performance in internal validation for predicting the risk of unplanned readmission among patients with moderate-to-severe COPD. However, these findings should be interpreted cautiously given the small sample size, single-center design. Future studies should use prospective, multicenter data from diverse geographic regions to externally validate and update the model.
SN - 1176-9106
SN - 1178-2005
ER -