Prevalence, Antimicrobial Resistance Profiles, and Virulence Determinants of Staphylococcus aureus Isolated from Hospitalized Patients

Abstract

Background: Staphylococcus aureus is an important bacterial pathogen associated with both community- and healthcare-associated infections. The increasing occurrence of methicillin-resistant S. aureus (MRSA), multidrug resistance, and virulence-associated determinants creates substantial challenges for antimicrobial therapy and infection-control programs. Conventional prevalence estimates provide point estimates but may not adequately represent uncertainty associated with heterogeneous hospital populations and variable resistance and virulence patterns. Objective: This study aimed to develop a Monte Carlo simulation model to estimate the prevalence of S. aureus, predict the proportion of MRSA, characterize antimicrobial resistance profiles, estimate multidrug resistance, and model the distribution of selected virulence determinants among hospitalized patients. Methods: A literature-informed probabilistic Monte Carlo model was developed for a hypothetical cohort of 500 hospitalized patients. Beta distributions were assigned to uncertain prevalence and resistance parameters, and 100,000 simulation iterations were performed. The model incorporated S. aureus prevalence, MRSA/MSSA classification, antimicrobial resistance, multidrug resistance, and selected resistance and virulence determinants, including mecA, mecC, pvl, hla, icaA, fnbA, sea, and tst. Results: The simulation estimated an overall S. aureus prevalence of 30.8%, corresponding to approximately 154 isolates per 500 hospitalized patients. MRSA represented 51.3% of simulated S. aureus isolates. Penicillin showed the highest modeled resistance, whereas vancomycin and linezolid showed comparatively low resistance. Approximately 49.7% of simulated S. aureus isolates were classified as multidrug resistant, with a higher proportion among MRSA. Among the modeled virulence determinants, hla and icaA showed relatively high frequencies, whereas mecC and tst were less frequent. Conclusion: The Monte Carlo model predicted a substantial burden of S. aureus, MRSA, antimicrobial resistance, multidrug resistance, and virulence-associated determinants among hospitalized patients. Probabilistic simulation provides a useful framework for representing uncertainty in hospital microbiological surveillance. The numerical findings represent modeled predictions and should not be interpreted as observations from a laboratory-based clinical study.