Evaluation of Emerging Antimicrobials Resistance in Nosocomial Infections Caused by E. coli: The Comparison Results of Observed Cases and Compartmental Model.
{"title":"Evaluation of Emerging Antimicrobials Resistance in Nosocomial Infections Caused by <i>E. coli</i>: The Comparison Results of Observed Cases and Compartmental Model.","authors":"Babak Eshrati, Elaheh Karimzadeh-Soureshjani, Mahshid Nasehi, Leila Janani, Hamid Reza Baradaran, Saeid Bitaraf, Pouria Ahmadi Simab, Sara Mobarak, Sasan Ghorbani Kalkhajeh, Mohammad Kogani","doi":"10.1155/ipid/3134775","DOIUrl":null,"url":null,"abstract":"<p><p><b>Background:</b> In recent years, the global rise of antibiotic-resistant <i>Escherichia coli</i> (<i>E. coli</i>) has become a significant threat to public health. This study aimed to identify and track outbreaks of antibiotic resistance, specifically among the antibiotics used to treat nosocomial <i>E. coli</i> infections. <b>Materials and Methods:</b> This hospital-based study utilized data from a nosocomial infection surveillance system to investigate reported cases of antibiotic resistance. The study analyzed the results of 12,954 antibiogram tests conducted across 57 hospitals in 31 provinces of Iran. The data was divided into two periods: the first and second halves of 2017. Before developing a predictive model for resistant <i>E. coli</i> cases, the model's validity was tested using the first half of the year's data. The predicted cases were then compared to the actual observed cases in 2017, with a statistically significant difference indicating an outbreak. <b>Findings:</b> The study found that, in 2017, hospitals in Iran experienced an outbreak of <i>E. coli</i> resistant to ampicillin and ceftazidime. This resistance was more prevalent than expected, highlighting the emergence of these drugs as major contributors to nosocomial <i>E. coli</i> infections. <b>Conclusion:</b> This study demonstrated the utility of the compartmental model in forecasting outbreaks of antibiotic-resistant <i>E. coli</i>. It provides a framework for investigating similar outbreaks in the future, using diverse data sources and methodologies.</p>","PeriodicalId":39128,"journal":{"name":"Interdisciplinary Perspectives on Infectious Diseases","volume":"2025 ","pages":"3134775"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11756951/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Interdisciplinary Perspectives on Infectious Diseases","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/ipid/3134775","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"Immunology and Microbiology","Score":null,"Total":0}
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Abstract
Background: In recent years, the global rise of antibiotic-resistant Escherichia coli (E. coli) has become a significant threat to public health. This study aimed to identify and track outbreaks of antibiotic resistance, specifically among the antibiotics used to treat nosocomial E. coli infections. Materials and Methods: This hospital-based study utilized data from a nosocomial infection surveillance system to investigate reported cases of antibiotic resistance. The study analyzed the results of 12,954 antibiogram tests conducted across 57 hospitals in 31 provinces of Iran. The data was divided into two periods: the first and second halves of 2017. Before developing a predictive model for resistant E. coli cases, the model's validity was tested using the first half of the year's data. The predicted cases were then compared to the actual observed cases in 2017, with a statistically significant difference indicating an outbreak. Findings: The study found that, in 2017, hospitals in Iran experienced an outbreak of E. coli resistant to ampicillin and ceftazidime. This resistance was more prevalent than expected, highlighting the emergence of these drugs as major contributors to nosocomial E. coli infections. Conclusion: This study demonstrated the utility of the compartmental model in forecasting outbreaks of antibiotic-resistant E. coli. It provides a framework for investigating similar outbreaks in the future, using diverse data sources and methodologies.