Leveraging Machine Learning and Real-World Data to Predict Chronic Obstructive Pulmonary Disease Exacerbations
Eosinophil count and dyspnea were identified as important predictors of exacerbations. Our data suggest that active monitoring of eosinophil counts and selected patient-reported experiences of dyspnea may identify patients at risk of exacerbations, enabling clinicians to tailor therapies to improve health outcomes among patients with COPD.
Authors
- Jason Roy (ORCID: https://orcid.org/0000-0001-5178-4210)
- Thomas G. O’Riordan
- Kristin Kahle‐Wrobleski
- Jason Attanucci
- Reynold A. Panettieri (ORCID: https://orcid.org/0000-0003-0834-4636)
- Allison Tyler
- Natalia Gontarczyk Uczkowski (ORCID: https://orcid.org/0009-0005-2919-2805)
Institutions
- Rutgers, The State University of New Jersey (US)
- California Institute of Technology (US)
Publication Details
- Journal
- International Journal of COPD
- Published
- 2025-10-01
- DOI
- https://doi.org/10.2147/copd.s536395
- Citations
- 1
- Primary Topic
- Chronic Obstructive Pulmonary Disease (COPD) Research
- Type
- article
- Field-Weighted Citation Impact
- 1.52