Risk factors for mortality in Acinetobacter baumannii bloodstream infections and development of a predictive

Silvia Corcione1, Bianca Maria Longo2, Silvia Scabini2

  • 1Department of Medical Sciences, Unit of Infectious Diseases, University of Turin, Italy; School of Medicine, Tufts University, Boston, Massachusetts, USA.

Abstract

Insights

Acinetobacter baumannii bloodstream infections have high mortality. The Sequential Organ Failure Assessment (SOFA) score predicts mortality, while burn injuries impact survival in these critical infections.

Area of Science:

  • Infectious Diseases
  • Critical Care Medicine
  • Antimicrobial Resistance

Background:

  • Acinetobacter baumannii bloodstream infections (BSIs) pose a significant public health threat due to high mortality and multidrug resistance.
  • Carbapenem-resistant Acinetobacter baumannii (CRAB) infections are particularly challenging, with limited treatment options.

Purpose of the Study:

  • To evaluate predictors of 14- and 30-day mortality in Acinetobacter baumannii BSIs.
  • To identify risk factors for CRAB BSIs.
  • To develop a predictive model for mortality in CRAB-related BSIs.

Main Methods:

  • Retrospective analysis of 126 adult patients with A. baumannii bacteremia between 2019 and 2023.
  • Multivariate analysis to identify mortality predictors and risk factors.
  • Development and internal validation of a predictive model for CRAB BSI mortality.

Main Results:

  • 89.7% of A. baumannii BSIs were CRAB.
  • Risk factors for CRAB BSI included burn injuries, older age, prior CRAB colonization, and antibiotic exposure.
  • 14-day mortality was 26.1%, and 30-day mortality was 30.9%.
  • Sequential Organ Failure Assessment (SOFA) score predicted both 14- and 30-day mortality.
  • Burn injuries correlated with 30-day survival; COVID-19 association with mortality was noted but not statistically significant.

Conclusions:

  • A. baumannii BSIs are associated with poor outcomes and limited therapeutic choices.
  • The SOFA score is a key predictor of mortality in these infections.
  • A validated predictive model can aid clinicians in identifying high-risk patients for improved decision-making.