| Title: |
Bayesian modeling of the impact of antibiotic resistance on the efficiency of MRSA decolonization |
| Authors: |
Ojala, Fanni; Abdul Sater, Mohamad R.; Miller, Loren G.; McKinnell, James A.; Hayden, Mary K.; Huang, Susan S.; Grad, Yonatan H.; Marttinen, Pekka |
| Contributors: |
Department of Computer Science; Professorship Marttinen P.; Computer Science Professors; Computer Science - Artificial Intelligence and Machine Learning (AIML) - Research area; Harvard School of Public Health; Lundquist Institute; Rush University; University of California, Irvine; Aalto-yliopisto; Aalto University |
| Publisher Information: |
Public Library of Science |
| Publication Year: |
2023 |
| Collection: |
Aalto University Publication Archive (Aaltodoc) / Aalto-yliopiston julkaisuarkistoa |
| Description: |
Funding Information: This work was supported by the Research Council of Finland (Flagship programme: Finnish Center for Artificial Intelligence FCAI, and grants 336033, 352986, 358246 to PM), EU (H2020 grant 101016775 and NextGenerationEU to PM), Doris Duke Charitable Foundation (grant no. 2016092 to YHG), R01HS019388 from the AHRQ Healthcare-Associated Infections Program (to SSH) and the University of California Irvine Institute for Clinical and Translational Science (NIH UL1 TR000153 to SSH). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Publisher Copyright: © 2023 Ojala et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | openaire: EC/H2020/101016775/EU//INTERVENE ; 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 ... |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
info:eu-repo/grantAgreement/EC/H2020/101016775/EU//INTERVENE; PLoS Computational Biology; Volume 19, issue 10, pp. 1-17; This work was supported by the Research Council of Finland (Flagship programme: Finnish Center for Artificial Intelligence FCAI, and grants 336033, 352986, 358246 to PM), EU (H2020 grant 101016775 and NextGenerationEU to PM), Doris Duke Charitable Foundation (grant no. 2016092 to YHG), R01HS019388 from the AHRQ Healthcare-Associated Infections Program (to SSH) and the University of California Irvine Institute for Clinical and Translational Science (NIH UL1 TR000153 to SSH). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.; PURE FILEURL: https://research.aalto.fi/files/128283896/SCI_Ojala_etal_PLOS_Computational_Biology_2023.pdf; https://aaltodoc.aalto.fi/handle/123456789/124668 |
| DOI: |
10.1371/journal.pcbi.1010898 |
| Availability: |
https://aaltodoc.aalto.fi/handle/123456789/124668; https://doi.org/10.1371/journal.pcbi.1010898 |
| Rights: |
openAccess |
| Accession Number: |
edsbas.D301C7BF |
| Database: |
BASE |