A machine learning model including pentraxin-3 as predictor of outcomes in community-acquired pneumonia.

ABSTRACT:

Background

The clinical diagnosis, severity assessment, and outcome prognostication of community-acquired pneumonia (CAP) remain challenging due to the complex disease pathophysiology. Accurate outcome prediction is crucial for optimizing patient management, reducing mortality, and minimizing hospital and ICU admissions.

Methods

In this prospective observational cohort study, 228 CAP patients with varying degrees of disease severity were assessed. Clinical and demographic data, along with multiple biomarker measurements, including pentraxin-3 (PTX3), were analysed longitudinally. The primary outcome was clinical failure.

Results

Among the single parameters evaluated, the oxygen saturation to fraction of inspired oxygen ratio (SpO₂/FiO₂), PTX3, an

SUBMITTER: Voza A 

PROVIDER: S-EPMC12579427 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature