Modeling and simulation of distribution and drug resistance of major pathogens in patients with respiratory system infections.

BACKGROUND: Respiratory tract infections (RTIs) are one of the leading causes of morbidity and mortality worldwide. The increase in antimicrobial resistance in respiratory pathogens poses a major challenge to the effective management of these infections.

OBJECTIVE: To investigate the distribution of major pathogens of RTIs and their antimicrobial resistance patterns in a tertiary care hospital and to develop a mathematical model to explore the relationship between pathogen distribution and antimicrobial resistance.

METHODS: Five hundred patients with RTIs were included in the study and 475 bacterial strains were isolated from their respiratory specimens. Antimicrobial susceptibility testing and analysis of influencing factors were performed. A mathematical model was developed to simulate the relationship between pathogen distribution and drug resistance.

RESULTS: The most common pathogens were Streptococcus pneumoniae (30%), Haemophilus influenzae (20%), Pseudomonas aeruginosa (15%), Staphylococcus aureus (10%) and Klebsiella pneumoniae (10%). The distribution of pathogens varied according to age group and type of RTIs, with higher proportions of Pseudomonas aeruginosa and Staphylococcus aureus in hospital-acquired and ventilator-associated pneumonia. Isolated pathogens showed high and increasing rates of resistance to commonly used antibiotics. Model simulations suggest that a shift in the distribution of pathogens toward more resistant strains may lead to a significant increase in overall resistance rates, even if antibiotic use patterns remain unchanged.

CONCLUSION: This study emphasizes the importance of regular monitoring of respiratory pathogen distribution and antimicrobial resistance patterns and the need for a comprehensive approach to managing RTIs, including implementation of antibiotic stewardship programs, infection control measures, and development of new therapies.