Impact of antibiotic usage on extended-spectrum β-lactamase producing Escherichia coli prevalence

Jeong Yeon Kim, Yunjin Yum, Hyung Joon Joo, Hyonggin An, Young Kyung Yoon, Jong Hun Kim, Jang Wook Sohn, Jeong Yeon Kim, Yunjin Yum, Hyung Joon Joo, Hyonggin An, Young Kyung Yoon, Jong Hun Kim, Jang Wook Sohn

Abstract

An increase in antibiotic usage is considered to contribute to the emergence of antimicrobial resistance. Although experts are counting on the antimicrobial stewardship programs to reduce antibiotic usage, their effect remains uncertain. In this study, we aimed to evaluate the impact of antibiotic usage and forecast the prevalence of hospital-acquired extended spectrum β-lactamase (ESBL)-producing Escherichia coli (E. coli) using time-series analysis. Antimicrobial culture information of E. coli was obtained using a text processing technique that helped extract free-text electronic health records from standardized data. The antimicrobial use density (AUD) of antibiotics of interest was used to estimate the quarterly antibiotic usage. Transfer function model was applied to forecast relationship between antibiotic usage and ESBL-producing E. coli. Of the 1938 hospital-acquired isolates, 831 isolates (42.9%) were ESBL-producing E. coli. Both the proportion of ESBL-producing E. coli and AUD increased over time. The transfer model predicted that ciprofloxacin AUD is related to the proportion of ESBL-producing E. coli two quarters later. In conclusion, excessive use of antibiotics was shown to affect the prevalence of resistant organisms in the future. Therefore, the control of antibiotics with antimicrobial stewardship programs should be considered to restrict antimicrobial resistance.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Flow diagram depicting the study population.
Figure 2
Figure 2
Time-series plot of ESBL-producing E. coli proportion and AUD from 1 January 2012 to 30 June 2019. AUD of (A) ciprofloxacin, (B) cefepime, (C) piperacillin-tazobactam and (D) third-generation cephalosporin.
Figure 3
Figure 3
Predicted and observed ESBL-producing E. coli proportion upon ciprofloxacin usage.

References

    1. Tacconelli E, et al. Discovery, research, and development of new antibiotics: the WHO priority list of antibiotic-resistant bacteria and tuberculosis. Lancet Infect. Dis. 2018;18:318–327. doi: 10.1016/S1473-3099(17)30753-3.
    1. Zhu FH, et al. Risk factors for community acquired urinary tract infections caused by extended spectrum β-lactamase (ESBL) producing Escherichia coli in children: a case control study. Infect. Dis. 2019;51:802–809. doi: 10.1080/23744235.2019.1654127.
    1. Tacconelli E, et al. Estimating the association between antibiotic exposure and colonization with extended-spectrum beta-lactamase-producing Gram-negative bacteria using machine learning methods: a multicentre, prospective cohort study. Clin. Microbiol. Infect. 2020;26:87–94. doi: 10.1016/j.cmi.2019.05.013.
    1. Schwartz B, Bell DM, Hughes JM. Preventing the emergence of antimicrobial resistance: a call for action by clinicians, public health officials, and patients. JAMA. 1997;278:944–945. doi: 10.1001/jama.1997.03550110082041.
    1. Murray MT, Beauchemin MP, Neu N, Larson EL. Prior antibiotic use and acquisition of multidrug-resistant organisms in hospitalized children: A systematic review. Infect. Control Hosp. Epidemiol. 2019;40:1107–1115. doi: 10.1017/ice.2019.215.
    1. Lee EJ, Lee G, Park J, Kim D-S, Ahn HS. Analysis of factors affecting antibiotic use at hospitals and clinics based on the defined daily dose. J. Korean Med. Assoc. 2018;61:687–698. doi: 10.5124/jkma.2018.61.11.687.
    1. Dyar OJ, Huttner B, Schouten J, Pulcini C, Esgap What is antimicrobial stewardship? Clin. Microbiol. Infect. 2017;23:793–798. doi: 10.1016/j.cmi.2017.08.026.
    1. Yoon YK, et al. Surveillance of antimicrobial use and antimicrobial resistance. J. Infect. Chemother. 2008;40:93–101. doi: 10.3947/ic.2008.40.2.93.
    1. Kim BN, Kim HB, Oh MD. Antibiotic control policies in South Korea, 2000–2013. Infect. Chemother. 2016;48:151–159. doi: 10.3947/ic.2016.48.3.151.
    1. Hyle EP, Bilker WB, Gasink LB, Lautenbach E. Impact of different methods for describing the extent of prior antibiotic exposure on the association between antibiotic use and antibiotic-resistant infection. Infect. Control Hosp. Epidemiol. 2007;28:647–654. doi: 10.1086/516798.
    1. Arepyeva MA, et al. A mathematical model for predicting the development of bacterial resistance based on the relationship between the level of antimicrobial resistance and the volume of antibiotic consumption. J. Glob. Antimicrob. Resist. 2017;8:148–156. doi: 10.1016/j.jgar.2016.11.010.
    1. Yoon YK, et al. Trends of antibiotic consumption in Korea according to national reimbursement data (2008–2012): A population-based epidemiologic study. Medicine (Baltimore) 2015;94:e2100. doi: 10.1097/MD.0000000000002100.
    1. Andersson DI, Hughes D. Antibiotic resistance and its cost: is it possible to reverse resistance? Nat. Rev. Microbiol. 2010;8:260–271. doi: 10.1038/nrmicro2319.
    1. Lopez-Lozano JM, et al. A nonlinear time-series analysis approach to identify thresholds in associations between population antibiotic use and rates of resistance. Nat. Microbiol. 2019;4:1160–1172. doi: 10.1038/s41564-019-0410-0.
    1. Otter JA, et al. Individual- and community-level risk factors for ESBL Enterobacteriaceae colonization identified by universal admission screening in London. Clin. Microbiol. Infect. 2019;25:1259–1265. doi: 10.1016/j.cmi.2019.02.026.
    1. Nakai H, et al. Prevalence and risk factors of infections caused by extended-spectrum β-lactamase (ESBL)-producing Enterobacteriaceae. J. Infect. Chemother. 2016;22:319–326. doi: 10.1016/j.jiac.2016.02.004.
    1. Hwang H, Kim B. Impact of an infectious diseases specialist-led antimicrobial stewardship programmes on antibiotic use and antimicrobial resistance in a large Korean hospital. Sci. Rep. 2018;8:14757. doi: 10.1038/s41598-018-33201-8.
    1. Seong K-T, Choi Y-H, Koo J-H, Lee M-J. Transfer function model forecasting of sea surface temperature at Yeosu in Korean coastal waters. J. Korean Soc. Mar. Environ. Saf. 2014;20:526–534. doi: 10.7837/kosomes.2014.20.5.526.
    1. Humphries RM, et al. CLSI methods development and standardization working group best practices for evaluation of antimicrobial susceptibility tests. J. Clin. Microbiol. 2018 doi: 10.1128/JCM.01934-17.
    1. ATC/DDD Index 2021. .

Source: PubMed

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