Estimating the impact of cefuroxime versus cefazolin and amoxicillin/clavulanate use on future collateral resistance: a retrospective comparison

Chowers, Michal; Zehavi, Tamir; Gottesman, Bat‐Sheva; Baraz, Avi; Nevo, Daniel; Obolski, Uri

doi:10.1093/jac/dkac130

Cited by 10 publications

(5 citation statements)

References 22 publications

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“…However, the relatively low aORs obtained for cephalosporins might be due to the aggregation of different cephalosporins administered to patients. 1st and 2nd generation cephalosporins are more widely used in the studied hospital, and are expected to confer lower resistance to ceftazidime, compared to 3rd generation cephalosporins [8,14].…”

Section: Discussionmentioning

confidence: 99%

The time-varying association between previous antibiotic use and antibiotic resistance

Baraz

Chowers

Nevo

et al. 2022

Preprint

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ObjectivesThe objective of the study was to estimate how the time elapsed from previous antibiotic use is associated with antibiotic resistance.MethodsData comprised electronic medical records of all patients in an Israeli hospital who had a positive bacterial culture between 2016-19. These included susceptibility testing results, and clinical and demographic data. Mixed-effects time-varying logistic models were fitted to estimate the association between time elapsed since last use of aminoglycosides and gentamicin resistance (n=13,094), cephalosporins and ceftazidime resistance (n=13,050), and fluoroquinolones and ciprofloxacin resistance (n=15,362), while adjusting for multiple covariates.ResultsFor all examined antibiotics, past antibiotic use had a statistically significant association with resistance (p<0.001). These associations exhibited a clear decreasing pattern over time, which we present as a flexible function of time. Nonetheless, previous antibiotic use remained a significant risk factor for resistance for at least 180 days, with adjusted odds ratios of 1.94 (95%CI 1.40-2.69), 1.33 (95%CI 1.10-1.61), and 2.25 (95%CI 1.49-3.41), for gentamicin, ceftazidime and ciprofloxacin, respectively.ConclusionsThe association between prior antibiotic use and resistance decreases over time. Commonly used cut-offs for prior antibiotic use can either misclassify patients still at higher risk, when too recent; or provide a diluted estimate of the effects of antibiotic use on future resistance, when too distant. Hence, prior antibiotic use should be considered as a time-dependent risk factor for resistance, both in epidemiological research and clinical practice.

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Section: Discussionmentioning

confidence: 99%

The time-varying association between previous antibiotic use and antibiotic resistance

Baraz

Chowers

Nevo

et al. 2022

Preprint

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“…Each use of an antibiotic drug depletes its future effectiveness due to antibiotic resistance [21, 22, 23, 27]. We assume that each use of a drug in the current period detracts its average effectiveness in the following period by a constant depletion effect, which may vary between drugs [26]. Formally, let be the initial effectiveness levels of drugs B, N respectively, where (i.e., the broad-spectrum drug is more likely to be effective).…”

Section: Modelmentioning

confidence: 99%

“…However, each use of an antibiotic increases the future resistance to it, and thus decreases its effectiveness [21, 22, 23]. In principle, each antibiotic treatment also exposes the patient to risks such as treatment side effects or increased likelihood of developing other, antibiotic resistant bacterial infections [24, 25, 26, 27]. Nonetheless, for simplicity, and since these risks are low relative to the immediate risk of not clearing the infection, we neglect these them.…”

Section: Introductionmentioning

confidence: 99%

The straight and narrow: a game theory model of broad- and narrow-spectrum empiric antibiotic therapy

Diamant¹,

Obolski²

2023

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Physicians prescribe empiric antibiotic treatment when definitive knowledge of the pathogen causing an infection is lacking. The options of empiric treatment can be largely divided into broad- and narrow-spectrum antibiotics. Prescribing a broad-spectrum antibiotic increases the chances of covering the causative pathogen, and hence benefits the current patient's recovery. However, prescription of broad-spectrum antibiotics also accelerates the expansion of antibiotic resistance, potentially harming future patients. We analyze the social dilemma using game theory. In our game model, physicians choose between prescribing broad and narrow-spectrum antibiotics to their patients. Their decisions rely on the probability of an infection by a resistant pathogen before definitive laboratory results are available. We prove that whenever the equilibrium strategies differ from the socially optimal policy, the deviation is always towards a more excessive use of the broad-spectrum antibiotic. We further show that if prescribing broad-spectrum antibiotics only to patients with a high probability of resistant infection is the socially optimal policy, then decentralization of the decision making may make this policy individually irrational, and thus sabotage its implementation. We discuss the importance of improving the probabilistic information available to the physician and promoting centralized decision making.

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“…Such inappropriate empirical treatment is associated with the prevalence of AMR 4 . Despite guidelines 5 , literature on collateral damage of antibiotics 5 , 6 , and stewardship initiatives 7 , the frequency of bug-drug mismatch in empiric treatment often remains high 4 , 8 .…”

Section: Introductionmentioning

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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Estimating the impact of cefuroxime versus cefazolin and amoxicillin/clavulanate use on future collateral resistance: a retrospective comparison

Cited by 10 publications

References 22 publications

The time-varying association between previous antibiotic use and antibiotic resistance

The time-varying association between previous antibiotic use and antibiotic resistance

The straight and narrow: a game theory model of broad- and narrow-spectrum empiric antibiotic therapy

Prediction of ciprofloxacin resistance in hospitalized patients using machine learning

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