Infection and drug resistance
Clinical Characteristics and Machine Learning-Based Severity Classification of Chlamydia psittaci Pneumonia: A Retrospective Cohort Study
Door Lei X, Zhao L, Zhong Z en 6 anderen.
Inhoud in het Engels
Abstract
Background
Chlamydia psittaci infection can cause severe community-acquired pneumonia with significant mortality. Distinguishing severe from non-severe disease remains challenging. This study used clinical data from patients with C. psittaci pneumonia and multiple machine-learning methods to develop a model for severity classification.
Methods
We retrospectively analyzed 231 hospitalized patients with C. psittaci pneumonia, including 84 severe cases, between January 2022 and April 2025 in Jiangxi Province, China. Severe pneumonia was defined according to the IDSA/ATS and Chinese adult community-acquired pneumonia criteria. The model outcome was the composite clinical label of severe versus non-severe pneumonia rather than mortality. A comprehensive machine-learning framework incorporating 11 algorithms and six feature-selection strategies was used to develop the severity-classification model.