Clinical characteristics and predictive models for hospitalized patients with COVID-19 combined with bacterial pneumonia

Man Yuan, Mei Liang, Jian Xu, Da He, Yanfang Zhang, Xiaoran Li, Jinzhi He, Yang Yang, Zhiyong Zong and Junyan Qu

PLOS ONE, 2025, vol. 20, issue 11, 1-12

Abstract: Background: This study aimed to analyze the clinical characteristics of hospitalized patients with COVID-19 combined with bacterial pneumonia and establish a predictive model to assist clinicians in the differential diagnosis and evaluation of the effectiveness of antibiotic treatment. Methods: In this retrospective study, we collected clinical, biochemical, imaging, and microbiological data from hospitalized patients with COVID-19 admitted to our hospital between December 1, 2022, and February 7, 2023. Univariate and multivariate analyses revealed independent risk factors for bacterial pneumonia in COVID-19 patients. Model performance was assessed via the area under the curve (AUC). Results: A total of 5358 COVID-19 patients were screened, and data from 1794 patients were ultimately included; 1386 patients had concomitant bacterial pneumonia (77.3%), whereas 408 patients served as controls (22.7%). Among COVID-19 patients, those with concomitant bacterial pneumonia had lower levels of albumin, hemoglobin, and lymphocyte ratio, along with higher levels of procalcitonin, globulin, glucose, urea, white blood cell count, and neutrophil ratio, than patients without bacterial pneumonia. Sputum cultures identified Acinetobacter baumanii, Klebsiella pneumoniae, and Pseudomonas aeruginosa as the top three bacterial species. A predictive model for the early detection of concomitant bacterial pneumonia in COVID-19 patients was developed via multivariate regression analysis, with an AUC of 0.850 (p

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0336668

DOI: 10.1371/journal.pone.0336668

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