Initial Antibiotic Selection Based on Microbiologic History in Pediatric Cystic Fibrosis-Related Pulmonary

Jillian Grapsy1, Ching-Sui Ueng1, Karisma Patel1

  • 1Department of Pharmacy, Children's Medical Center Dallas, Dallas, Texas, USA.

Pediatric Pulmonology
|February 3, 2025
PubMed
Abstract

Insights

Pediatric cystic fibrosis (CF) patients

Area of Science:

  • Pediatric pulmonology
  • Infectious diseases
  • Microbiology

Background:

  • Cystic Fibrosis (CF) Foundation guidelines lack specifics on empiric antibiotic selection for pulmonary exacerbations (PEx).
  • Understanding current practices in antibiotic selection for CF-related PEx is crucial for optimizing treatment.
  • This study addresses this gap by examining antibiotic selection based on patient history.

Purpose of the Study:

  • To characterize the utilization of patient-specific microbiological histories in initial antibiotic selection for CF-related PEx in pediatric patients.
  • To identify reasons for changes made to empiric antibiotic regimens during hospitalization.

Main Methods:

  • Retrospective single-center study of pediatric patients (1-21 years) hospitalized for CF-related PEx.
  • Data collected from Children's Medical Center Dallas between August 2016 and July 2018.
  • Analysis of 156 patient encounters meeting inclusion criteria.

Main Results:

  • Staphylococcus aureus, Pseudomonas aeruginosa, and Stenotrophomonas maltophilia were frequently targeted organisms.
  • Median time since last growth varied for targeted vs. non-targeted organisms, with significant overlap.
  • Initial antibiotic regimens demonstrated sensitivity to admission culture isolates in 78.2% of cases.

Conclusions:

  • Patient-specific microbiological history, including time since last organism growth, influences initial antibiotic choices for CF PEx.
  • Extending historical organism surveillance beyond one year did not improve coverage for admission isolates in CF patients.
  • Current practices show variability but consider historical microbiological data in empiric antibiotic selection.

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