A Computational Approach to Evaluating Empirical Antibiotic Coverage for Gram-Negative Bloodstream Infections in Pediatric Febrile Neutropenia

Abstract

Background: Empirical antibacterial therapy for febrile neutropenia requires adaptation to local epidemiology, a process that is often complex, time-consuming, and prone to human error. This study aims to address this challenge by developing a practical, data-driven tool to efficiently evaluate and adapt treatment protocols. Methods: We developed a novel, open-source computational script in Python (version 3.10), aided by large language models for code revision, to analyze antibiotic susceptibility data. The script was validated using a retrospective dataset of 237 Gram-negative bloodstream infections (BSIs) from 2015 to 2024 in cancer or hematopoietic stem cell transplant recipients at a tertiary care pediatric hospital in Italy. The script calculates efficacy metrics for both single agents and two-drug combinations. Results: Among the Gram-negative BSI strains analyzed, meropenem monotherapy demonstrated the highest efficacy (median 95.4%). In contrast, piperacillin/tazobactam and cefepime showed lower efficacy (80.3% and 81.8%, respectively). On the contrary, combination therapy, particularly with amikacin, significantly increased the efficacy of beta-lactams, elevating their effectiveness to a level comparable to meropenem. Conclusions: The developed script is a valuable tool for antimicrobial stewardship programs, offering a rapid and accessible method to validate international guidelines against local epidemiological data. While meropenem shows high efficacy, its broad use should be limited to prevent resistance. The combination of piperacillin-tazobactam and amikacin is identified as a robust and effective empirical treatment choice.

Keywords: antibiotic therapy; computational script; febrile neutropenia.

Figure 1

Python script workflow diagram.