Impact of Baseline Characteristics on Future Episodes of Bloodstream Infections: Multistate Model in Septic Patients with Bloodstream Infections

M. Cristina Vazquez Guillamet, Rodrigo Vazquez, Jonas Noe, Scott T. Micek, Victoria J. Fraser, Marin H. Kollef

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Background: Looking only at the index infection, studies have described risk factors for infections caused by resistant bacteria. We hypothesized that septic patients with bloodstream infections may transition across states characterized by different microbiology and that their trajectory is not uniform. We also hypothesized that baseline risk factors may influence subsequent blood culture results. Methods: All adult septic patients with positive blood cultures over a 7-year period were included in the study. Baseline risk factors were recorded. We followed all survivors longitudinally and recorded subsequent blood culture results. We separated states into bacteremia caused by gram-positive cocci, susceptible gram-negative bacilli (sGNB), resistant GNB (rGNB), and Candida spp. Detrimental transitions were considered when transitioning to a culture with a higher mortality risk (rGNB and Candida spp.). A multistate Markov-like model was used to determine risk factors associated with detrimental transitions. Results: A total of 990 patients survived and experienced at least 1 transition, with a total of 4282 transitions. Inappropriate antibiotics, previous antibiotic exposure, and index bloodstream infection caused by either rGNB or Candida spp. were associated with detrimental transitions. Double antibiotic therapy (beta-lactam plus either an aminoglycoside or a fluoroquinolone) protected against detrimental transitions. Conclusion: Baseline characteristics that include prescribed antibiotics can identify patients at risk for subsequent bloodstream infections caused by resistant bacteria. By altering the initial treatment, we could potentially influence future bacteremic states.

Original languageEnglish
Pages (from-to)3103-3109
Number of pages7
JournalClinical Infectious Diseases
Volume71
Issue number12
DOIs
StatePublished - Dec 15 2020

Keywords

  • Markov model
  • bloodstream infections
  • combination therapy
  • sepsis
  • transitions

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