Machine learning based prediction of antimicrobial resistance in Klebsiella spp .: a five-year retrospective study.
XGBoost model predicted Klebsiella antimicrobial resistance with 70% accuracy.
Machine learning based prediction of antimicrobial resistance in Klebsiella spp .: a five-year retrospective study.
Klebsiella species are well-recognized pathogens implicated in both healthcare-associated and community-acquired infections, and they contribute substantially to the global burden of antimicrobial resistance.
A retrospective analysis conducted using routine microbiology laboratory data collected between 2019 and 2024.
Over the five-year study period, Klebsiella species accounted for 2,646 isolates (13.7%) of all clinical isolates, with Klebsiella pneumoniae representing the predominant species.
Urinary tract infections were the most frequent source (45.5%), followed by blood cultures (15.4%), respiratory samples (14%), and wound and soft tissue specimens (12.2%).
High resistance rates were observed for ceftazidime, whereas most other antibiotics demonstrated moderate resistance levels.
Tigecycline, and Ceftriaxone showed the lowest resistance rates.
Collectively, these findings underscore the increasing clinical burden of Klebsiella infections and demonstrate the potential of boosting-based machine learning algorithms, particularly XGBoost and Light Gradient Boosting Machine, for accurate prediction of antibiotic resistance.
Integration of these models into clinical decision-support systems and antimicrobial stewardship programs may facilitate timely and appropriate antimicrobial therapy, thereby improving patient outcomes and promoting more rational antibiotic use.