Patterns of antibiotic cross-resistance by bacterial sample source: a retrospective cohort study

Cherny, Stacey S.; Chowers, Michal; Obolski, Uri

doi:10.1101/2022.03.31.22273223

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…Previous resistance to ciprofloxacin is an obvious risk factor for current resistance 20,51,52 . However, the importance of previous resistance to other antibiotics may be explained by crossresistance [53][54][55] , or confounding by the patients' exposure to resistant bacteria or to antibiotics. Patients' origin (home, another hospital, nursing home, medical clinic, or other) had substantial influence on predictions and was also found to be an important variable by others 22,56 .…”

Section: Discussionmentioning

confidence: 99%

Prediction of ciprofloxacin resistance in hospitalized patients using machine learning

2023

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Background Ciprofloxacin is a widely used antibiotic that has lost efficiency due to extensive resistance. We developed machine learning (ML) models that predict the probability of ciprofloxacin resistance in hospitalized patients. Methods Data were collected from electronic records of hospitalized patients with positive bacterial cultures, during 2016-2019. Susceptibility results to ciprofloxacin (n = 10,053 cultures) were obtained for Escherichia coli, Klebsiella pneumoniae, Morganella morganii, Pseudomonas aeruginosa, Proteus mirabilis and Staphylococcus aureus. An ensemble model, combining several base models, was developed to predict ciprofloxacin resistant cultures, either with (gnostic) or without (agnostic) information on the infecting bacterial species. Results The ensemble models’ predictions are well-calibrated, and yield ROC-AUCs (area under the receiver operating characteristic curve) of 0.737 (95%CI 0.715–0.758) and 0.837 (95%CI 0.821–0.854) on independent test-sets for the agnostic and gnostic datasets, respectively. Shapley additive explanations analysis identifies that influential variables are related to resistance of previous infections, where patients arrived from (hospital, nursing home, etc.), and recent resistance frequencies in the hospital. A decision curve analysis reveals that implementing our models can be beneficial in a wide range of cost-benefits considerations of ciprofloxacin administration. Conclusions This study develops ML models to predict ciprofloxacin resistance in hospitalized patients. The models achieve high predictive ability, are well calibrated, have substantial net-benefit across a wide range of conditions, and rely on predictors consistent with the literature. This is a further step on the way to inclusion of ML decision support systems into clinical practice.

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Section: Discussionmentioning

confidence: 99%

Prediction of ciprofloxacin resistance in hospitalized patients using machine learning

2023

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“…Highly influential variables on the ensemble models' predictions were related to previous infections containing resistant bacteria, either to ciprofloxacin or other antibiotics. Whereas previous resistance to ciprofloxacin is an obvious risk factor for current resistance, the importance of previous resistance to other antibiotics may be explained by cross-resistance (39)(40)(41), or confounding by the patients' exposure to resistant bacteria or to antibiotics. Patients' origin (home, another hospital, nursing home, medical clinic, or other) had substantial influence on predictions and was also found to be an important variable by others (22,42).…”

Section: Discussionmentioning

confidence: 99%

Prediction of Ciprofloxacin Resistance in Hospitalized Patients Using Machine Learning

Mintz

Chowers

Obolski

2022

Preprint

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Background: Ciprofloxacin is a widely used antibiotic that has lost efficiency due to extensive resistance. We developed machine learning (ML) models that predict the probability of ciprofloxacin resistance in hospitalized patients. Methods: Data were collected from electronic records of hospitalized patients with positive bacterial cultures, during 2016-2019. Susceptibility results to ciprofloxacin (n=10,053 cultures) were obtained for E. coli, K. pneumoniae, M. morganii, P.aeruginosa, P. mirabilis and S. aureus. An ensemble model, combining several base models, was developed to predict ciprofloxacin resistant cultures, either with (gnostic) or without (agnostic) information on the infecting bacterial species. Results: The ensemble models' predictions were well-calibrated, and yielded ROC-AUCs (area under the receiver operating characteristic curve) of 0.763 (95%CI 0.634-0.785) and 0.849 (95%CI 0.799-0.921) on independent test-sets for the agnostic and gnostic datasets, respectively. Shapley additive explanations analysis identified that influential variables were related to resistance of previous infections, where patients arrived from (hospital, nursing home, etc.), sex, and recent resistance frequencies in the hospital. A decision curve analysis revealed that implementing our models can be beneficial in a wide range of cost-benefits considerations of ciprofloxacin administration. Conclusions: This study develops ML models to predict ciprofloxacin resistance in hospitalized patients. The models achieved high predictive ability, were well calibrated, had substantial net-benefit across a wide range of conditions, and relied on predictors consistent with the literature. This is a further step on the way to inclusion of ML decision support systems into clinical practice.

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“…Additionally, the absence of quantitative data hampers AMR surveillance efforts, indicating the necessity of a better understanding to reduce resistance spread from the environment to clinical settings [ 8 ]. Bacteria from different sources may exhibit distinct resistance patterns due to varying evolutionary trajectories from different selective forces [ 9 ], influencing antibiotic treatment decisions. Due to their widespread occurrence in the environment, along with their ability to serve as reservoirs of antibiotic resistance genes, Escherichia coli ( E. coli ) is commonly employed as an indicator in numerous antimicrobial resistance monitoring programs [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Examining Antimicrobial Resistance in Escherichia coli: A Case Study in Central Virginia’s Environment

Kim,

Riley,

Sriharan

et al. 2024

Antibiotics

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While environmental factors may contribute to antimicrobial resistance (AMR) in bacteria, many aspects of environmental antibiotic pollution and resistance remain unknown. Furthermore, the level of AMR in Escherichia coli is considered a reliable indicator of the selection pressure exerted by antimicrobial use in the environment. This study aimed to assess AMR variance in E. coli isolated from diverse environmental samples, such as animal feces and water from wastewater treatment plants (WWTPs) and drainage areas of different land use systems in Central Virginia. In total, 450 E. coli isolates obtained between August 2020 and February 2021 were subjected to susceptibility testing against 12 antimicrobial agents approved for clinical use by the U.S. Food and Drug Administration. Approximately 87.8% of the tested isolates were resistant to at least one antimicrobial agent, with 3.1% showing multi-drug resistance. Streptomycin resistance was the most common (73.1%), while susceptibility to chloramphenicol was the highest (97.6%). One isolate obtained from WWTPs exhibited resistance to seven antimicrobials. AMR prevalence was the highest in WWTP isolates, followed by isolates from drainage areas, wild avians, and livestock. Among livestock, horses had the highest AMR prevalence, while cattle had the lowest. No significant AMR difference was found across land use systems. This study identifies potential AMR hotspots, emphasizing the environmental risk for antimicrobial resistant E. coli. The findings will aid policymakers and researchers, highlighting knowledge gaps in AMR–environment links. This nationally relevant research offers a scalable AMR model for understanding E. coli ecology. Further large-scale research is crucial to confirm the environmental impacts on AMR prevalence in bacteria.

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Patterns of antibiotic cross-resistance by bacterial sample source: a retrospective cohort study

Cited by 3 publications

References 21 publications

Prediction of ciprofloxacin resistance in hospitalized patients using machine learning

Prediction of ciprofloxacin resistance in hospitalized patients using machine learning

Prediction of Ciprofloxacin Resistance in Hospitalized Patients Using Machine Learning

Examining Antimicrobial Resistance in Escherichia coli: A Case Study in Central Virginia’s Environment

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