Bayesian modeling of the impact of antibiotic resistance on the efficiency of MRSA decolonization

PLoS Comput Biol. 2023 Oct 26;19(10):e1010898. doi: 10.1371/journal.pcbi.1010898. eCollection 2023 Oct.


Methicillin-resistant Staphylococcus aureus (MRSA) is a major cause of morbidity and mortality. Colonization by MRSA increases the risk of infection and transmission, underscoring the importance of decolonization efforts. However, success of these decolonization protocols varies, raising the possibility that some MRSA strains may be more persistent than others. Here, we studied how the persistence of MRSA colonization correlates with genomic presence of antibiotic resistance genes. Our analysis using a Bayesian mixed effects survival model found that genetic determinants of high-level resistance to mupirocin was strongly associated with failure of the decolonization protocol. However, we did not see a similar effect with genetic resistance to chlorhexidine or other antibiotics. Including strain-specific random effects improved the predictive performance, indicating that some strain characteristics other than resistance also contributed to persistence. Study subject-specific random effects did not improve the model. Our results highlight the need to consider the properties of the colonizing MRSA strain when deciding which treatments to include in the decolonization protocol.

MeSH terms

  • Anti-Bacterial Agents / pharmacology
  • Anti-Infective Agents, Local*
  • Bayes Theorem
  • Carrier State
  • Drug Resistance, Microbial
  • Humans
  • Methicillin-Resistant Staphylococcus aureus*
  • Staphylococcal Infections* / drug therapy


  • Anti-Infective Agents, Local
  • Anti-Bacterial Agents