ArticleClinical and experimental medicine2024
Clinical characteristics and prognosis analysis of pseudomonas aeruginosa bloodstream infection in adults: a retrospective study.
Article in Clinical and experimental medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Female Sex and Mortality in Patients With Gram-Negative Bacteremia: A Systematic Review and Meta-Analysis.JAMA network open · 2025Pooled it
- First reported Pseudomonas soli bloodstream infection confirmed by whole-genome sequencing after MALDI-TOF misidentification.BMC microbiology · 2026Article
- Clinical distribution and drug resistance of carbapenem-resistant Pseudomonas aeruginosa infection in Eastern China from 2019 to 2024.BMC infectious diseases · 2026Article
- Genomic characteristics and risk factor analysis of carbapenem-non-susceptible Pseudomonas aeruginosa causing bacteremia in a tertiary hospital (2014-2023).BMC microbiology · 2026Article
- Genetic Determinants Linked to MDR/XDR inPathogens (Basel, Switzerland) · 2026Article
- Predictors of Mortality inPathogens (Basel, Switzerland) · 2026Review
- Pseudomonas aeruginosa bloodstream infections in internal medicine wards: A large Italian multicenter retrospective study.PloS one · 2025Article
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8 authors.
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Abstract
Pseudomonas aeruginosa bloodstream infections (PA BSIs) in adults, especially those complicated by sepsis, are associated with high rates of morbidity and mortality. Early identification of risk factors for both mortality and sepsis-induced coagulopathy (SIC) is critical to optimizing patient management and improving outcomes. We conducted a retrospective analysis of 118 adult patients diagnosed with PA BSIs at the Affiliated Hospital of Xuzhou Medical University from January 2022 to February 2024. Univariate analysis was employed to identify significant clinical factors, followed by multivariate stepwise logistic regression to determine independent predictors of mortality and SIC. Based on these findings, nomogram models were constructed and evaluated using the area under the receiver operating characteristic curve (AUC), Bootstrap resampling, and calibration plots to assess model performance. Empiric sensitive antibiotic therapy (ESAT) (OR = 0.039, P < 0.001), coronary artery disease (CAD) (OR = 10.315, P = 0.010), and invasive mechanical ventilation (OR = 3.926, P = 0.020) emerged as significant predictors of mortality. In contrast, elevated C-reactive protein (CRP) (OR = 1.011, P = 0.003), procalcitonin (PCT) (OR = 1.030, P = 0.005), and lower hemoglobin levels (OR = 0.963, P = 0.004) were independently associated with SIC. The AUC of mortality prediction model is 0.908, while the SIC prediction model yielded an AUC of 0.817. The predictive models developed in this study demonstrate early identification of mortality rates and SIC risk in PA BSI patients, which may have the potential to guide timely therapeutic interventions and improve clinical outcomes in this high-risk population.
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