AUTHOR=Zhou Zhitong , Liu Shangshu , Qu Fangzhou , Wei Yuanhui , Song Manya , Guan Xizhou 

TITLE=Development and validation of a clinical prediction model for pneumonia - associated bloodstream infections

JOURNAL=Frontiers in Cellular and Infection Microbiology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1531732

DOI=10.3389/fcimb.2025.1531732

ISSN=2235-2988

ABSTRACT=PurposeThe aim of this study was to develop a valuable clinical prediction model for pneumonia-associated bloodstream infections (PABSIs).Patients and methodsThe study retrospectively collected clinical data of pneumonia patients at the First Medical Centre of the Chinese People’s Liberation Army General Hospital from 2019 to 2024. Patients who met the definition of pneumonia-associated bloodstream infections (PABSIs) were selected as the main research subjects. A prediction model for the probability of bloodstream infections (BSIs) in pneumonia patients was constructed using a combination of LASSO regression and logistic regression. The performance of the model was verified using several indicators, including receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA) and cross validation.ResultsA total of 423 patients with confirmed pneumonia were included in the study, in accordance with the Inclusion Criteria and Exclusion Criteria. Of the patients included in the study, 73 developed a related bloodstream infection (BSI). A prediction model was constructed based on six predictors: long-term antibiotic use, invasive mechanical ventilation, glucocorticoids, urinary catheterization, vasoactive drugs, and central venous catheter placement. The areas under the curve (AUC) of the training set and validation set were 0.83 and 0.80, respectively, and the calibration curve demonstrated satisfactory agreement between the two.ConclusionThis study has successfully constructed a prediction model for bloodstream infections associated with pneumonia cases, which has good stability and predictability and can help guide clinical work.