Transforming post-acute pneumonia management: risk stratification using machine learning
Abstract
Extract Community-acquired pneumonia (CAP) remains an important cause of morbidity and mortality worldwide, particularly among high-risk populations such as young children, immunosuppressed patients and elderly individuals with multiple comorbidities [1–3]. Advances in the diagnosis, antimicrobial treatment and general management of CAP has improved short-term prognosis [4]. However, the long-term sequelae, and particularly post-acute mortality in pneumonia are also frequently underestimated and neglected [5]. For example, a study from Spain highlights that 1-year mortality after hospitalisation for CAP is significant. 7.2% (93 out of 1284 patients) died within one year, with the most deaths occurring within the first 6 months. Early re-admission and comorbidities such as COPD, diabetes mellitus, cancer and dementia were strong predictors of long-term mortality [6].
Fields of Study & Subjects
Machine Learning in Healthcare
Sepsis Diagnosis and Treatment
Pneumonia and Respiratory Infections
Publication details
- Journal / source
- ERJ Open Research
- Publisher
- European Respiratory Society (ERS)
- Volume
- 12
- Issue
- 1
- Pages
- 00920–2025
- Publication date
- 1 January 2026
- Language
- EN