STRENGTHENING THE ROADMAP OF ANTIMICROBIAL STEWARDSHIP/ EVOLUTION OF ARTIFICIAL INTELLIGENCE

Year 2024, Volume: 5 Issue: 3, 131 - 136, 29.07.2024

Sumeyra Kocturk

https://doi.org/10.46871/eams.1431345

Abstract

Systematic approaches to improving the use of antimicrobials across the spectrum of healthcare are referred to as antimicrobial stewardship. Most studies of antimicrobial stewardship program strategies have addressed the mandated use of antimicrobials, the cost of antimicrobials, and rates of antimicrobial resistance. However, many of these studies have been insufficiently powered to detect differences between groups in clinical cure rates, length of hospital stay, and mortality rates; often there was frequently been no difference between the antimicrobial stewardship group and the control groups (2).

While questioning the implementation of these strategies; each government regulates institutions to track and also report stewardship metrics. Instutions uses the data analysis programs include all antimicrobial sensitivity tests confirmed and completed in the laboratory(1). It should provide the ability to obtain test results along with the requirements listed below. Preferably, the system should be able to display the sample results even if antimicrobial testing of the detected microorganisms (if any) has not been performed (3). For identification of specimens sent to laboratories for

Keywords

antimicrobial resistance, antimicrobial stewardship, artificial intelligece

References

• 1. Antimicrobial report- Bracing for Superbugs. UN environment reports United Nations Environment Programme. 2023;978-92-807-4006-6.
• 2. Baur D, Gladstone BP, Burkert F , et al. Effect of antibiotic stewardship on the incidence of infection and colonisation with antibiotic-resistant bacteria and Clostridium difficile infection:a systeatic review and meta-analysis. The Lancet infect. Disease. 2017;17(9) :990-1001.
• 3. A guide to antibiotic susceptibility data analysis and presentation formedical microbiologists. Klimud(ClinicalMicrobiologyAssociation)publications.2019; 15-20.
• 4. Kohlmann R., Gatermann SG. Analysis and presentation of cumulative antimicrobial susceptibility test data- The influence of different parameters in a routine clinical microbiology laboratory. Plos One 2016; 11(1).
• 5. Liu YY, Wang Y, Walsh TR, et al. Emergence of plasmid-mediated colistin resistance mechanism MCR-1 in animals and human beings in China: a microbiological and molecular biological study. Lancet Infect. Dis. 2016;16, 161–8.
• 6. Lübbert C, Baars C, Dayakar A, et al. Environmental pollution with antimicrobial agents from bulk drug manufacturing industries in Hyderabad, South India, is associated with dissemination of extended-spectrum β-lactamase and carbapenemase- 2017 Aug; 45(4): 479-491.
• 7. Review on antimicrobial resistance. Tackling drug- resistant infections globally: final report and recommendations /AMR, 2016.
• 8. World Health Organization Library Cataloguing inPublication Data. Global action plan on antimicrobial resistance /WHO, 2015.
• 9. Rolfe R,Jr, Kwobah C, Muro F, et al. Barriers to implementing antimicrobial stewardship programs in three low- and middle-income country tertiary care settings: findings from a multi-site qualitative study. Antimicrob Resist Infect Control. 2021; 10:60.
• 10. Belkum AV, Bachmann TT, Lüdke G. Developmental roadmap for antimicrobial susceptibility testing systems.Nature Reviews Microbiology, volume /2019; 17:50-5.
• 11. Feretzakis G, Loupelis E, Sakagianni A, et al. Using Machine Learning Algorithms to Predict Antimicrobial Resistance and Assist Empirical Treatment The Importance of Health Informatics in Public Health during a Pandemic. 2020 June. doi:10.3233/SHTI200497.
• 12. Cavallaro M., Moran E., Collyer B., et al. Informing antimicrobial stewardship with explainable AI. Plos Digital Health 2023 Jan 5. https://doi.org/10.1371/journal.pdig.0000162.
• 13. Croxatto A, Prod’hom G, Greub. Applications of MALDI-TOF Mass Spectrometry in Clinical Diagnostic Microbiology. FEMS Microbiol Rev. 2012;36(2): 380-407.
• 14. Wieser A, Schneider L, Jung J,et al. MALDI-TOF MS in Microbiological Diagnostics Identification of Microorganisms and Beyond. Appl Microbiol Biotechnol. 2012;93(3):965-74.
• 15. Anhalt JP, Fenselau C. Identification of Bacteria Using Mass Spectrometry. Anal Chem. 1975;47: 219-25.
• 16. Nguyen M, Long SW, McDermott PF, et al. Using Machine Learning to predict antimicrobial MICs and associated genomic features for Nontyphoidal Salmonella. J Clin Microbiol. 2019, 30;57(2):e01260-18.
• 17. Alimadadi A, Aryal S, Manandhar I, et al. Artificial Intelligence and Machine Learning to Fight COVID-19. Physiol Genomics. 2020 Apr 1;52(4):200-2.
• 18. Altindis M. Yapay zekanın mikrobiyolojide kullanımı. Sağlıkta yapay zeka. Medipol Üniversitesi yayınları 2021-58.