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Updated: May 27, 2026
Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model
Published on: April 6, 2021
A model to predict mortality following Pseudomonas aeruginosa bacteremia
Elizabeth B Hirsch1, Jessica M Cottreau, Kai-Tai Chang
1University of Houston College of Pharmacy, Houston, TX 77030, USA.
Abstract:
Infections caused by Pseudomonas aeruginosa are associated with significant mortality. Existing mathematical models identifying mortality risk factors lack validation. We developed and validated a model to predict mortality in patients with P. aeruginosa bacteremia. Risk factors for 30-day mortality were examined through multivariate logistic regression in 114 patients. Independent predictors of mortality included isolation of a multidrug-resistant strain, APACHE II ≥ 23, and age ≥ 65 years. Clonality was assessed for multidrug-resistant isolates. Predicted probability of 30-day mortality was validated in 49 patients, after conditioning the model by the identified risk factors. The patients were split into 'high-risk' and 'low-risk' groups based on model-predicted mortality; the observed/expected ratios were 1.21 and 1.92, respectively. Our model was reasonable in predicting 30-day mortality in patients with P. aeruginosa bacteremia. Our results may be useful for developing strategies to reduce mortality attributed to P. aeruginosa.
Insights
Predicting mortality in Pseudomonas aeruginosa bacteremia is crucial. A validated model identifies multidrug-resistant strains, high APACHE II scores, and older age as key risk factors for patient survival.
Area of Science:
- Infectious Diseases
- Clinical Microbiology
- Biostatistics
Background:
- Pseudomonas aeruginosa infections are a major cause of mortality.
- Existing models for predicting mortality lack robust validation.
- Effective risk stratification is needed for P. aeruginosa bacteremia management.
Purpose of the Study:
- To develop and validate a predictive model for 30-day mortality in patients with P. aeruginosa bacteremia.
- To identify independent risk factors associated with P. aeruginosa bacteremia mortality.
- To stratify patients into risk groups for targeted interventions.
Main Methods:
- Multivariate logistic regression analysis of 114 patients with P. aeruginosa bacteremia.
- Identification of independent predictors of 30-day mortality.
- Validation of the predictive model in an independent cohort of 49 patients.
- Assessment of clonality in multidrug-resistant isolates.
Main Results:
- Independent predictors of 30-day mortality included multidrug-resistant P. aeruginosa, APACHE II score ≥ 23, and age ≥ 65 years.
- The validated model demonstrated reasonable predictive performance.
- Observed/expected mortality ratios were 1.21 for the low-risk group and 1.92 for the high-risk group.
- Clonality analysis provided insights into the transmission of resistant strains.
Conclusions:
- The developed model effectively predicts 30-day mortality in P. aeruginosa bacteremia.
- Identifying high-risk patients enables tailored treatment strategies.
- This validated model can aid in reducing mortality associated with P. aeruginosa infections.
