Leveraging Machine Learning and Real-World Data to Predict Chronic Obstructive Pulmonary Disease Exacerbations
Reynold A Panettieri1, Jason Roy2, Natalia Gontarczyk Uczkowski1
1Rutgers Institute for Translational Medicine and Science, New Brunswick, NJ, USA.
International Journal of Chronic Obstructive Pulmonary Disease
|November 6, 2025
Summary
Artificial intelligence identified key predictors for chronic obstructive pulmonary disease (COPD) exacerbations. Monitoring eosinophil counts and dyspnea can help identify at-risk patients for tailored COPD treatment.
Area of Science:
- Pulmonary Medicine
- Medical Informatics
- Machine Learning
Background:
- Previous research demonstrated AI and NLP's utility in identifying patients at risk of chronic obstructive pulmonary disease (COPD) exacerbations using EHR data.
- This study builds upon prior work by developing a predictive model for COPD exacerbations.
Purpose of the Study:
- To establish a predictive model for identifying patients at risk of COPD exacerbations within 24 months of diagnosis.
- To leverage real-world electronic health record (EHR) data for improved patient risk stratification.
Main Methods:
- Utilized structured and unstructured data from Epic EHR.
- Employed Bayesian Additive Regression Trees (BART), a flexible machine-learning approach, for multivariable prediction.
- Assessed model performance using receiver operating characteristic (ROC) curves and area under the ROC curve (AUC).
Main Results:
- Analysis included 3007 patients; 886 experienced exacerbations within 24 months.
- Bivariate analyses showed associations between exacerbations and cor pulmonale, dyspnea, and comorbidities.
- The BART model identified eosinophil count, pack years, and dyspnea as key predictors, achieving an AUC of 0.69.
Conclusions:
- Eosinophil count and dyspnea are significant predictors of COPD exacerbations.
- Active monitoring of these factors can help identify high-risk patients.
- Tailored therapies based on identified risk factors may improve health outcomes for COPD patients.
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