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

OBJECTIVES: The objective of the study was to estimate how the time elapsed from previous antibiotic use is associated with antibiotic resistance. METHODS: Data comprised electronic medical records of all patients in an Israeli hospital who had a positive bacterial culture from 2016 to 2019. These included susceptibility testing results and clinical and demographic data. Mixed-effects time-varying logistic models were fitted to estimate the association between the time elapsed since the last use of aminoglycosides and gentamicin resistance (n = 13 095), cephalosporins and ceftazidime resistance (n = 13 051), and fluoroquinolones and ciprofloxacin resistance (n = 15 364) while adjusting for multiple covariates. RESULTS: For all examined antibiotics, previous 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 when the adjusted ORs were 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. DISCUSSION: The 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 a time-dependent risk factor for resistance in both epidemiological research and clinical practice.

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

BACKGROUND: Quantitative estimates of collateral resistance induced by antibiotic use are scarce. OBJECTIVES: To estimate the effects of treatment with amoxicillin/clavulanate or cefazolin, compared with cefuroxime, on future resistance to ceftazidime among hospitalized patients. METHODS: A retrospective analysis of patients with positive bacterial cultures hospitalized in an Israeli hospital during 2016-19 was conducted. Patients were restricted to those treated with amoxicillin/clavulanate, cefazolin or cefuroxime and re-hospitalized with a positive bacterial culture during the following year. Matching was performed using exact, Mahalanobis and propensity score matching. Each patient in the amoxicillin/clavulanate and cefazolin groups was matched to a single patient from the cefuroxime group, yielding 185:185 and 298:298 matched patients. Logistic regression and the g-formula (standardization) were used to estimate the OR, risk difference (RD) and number needed to harm (NNH). RESULTS: Cefuroxime induced significantly higher resistance to ceftazidime than amoxicillin/clavulanate or cefazolin; the marginal OR was 1.76 (95% CI = 1.16-2.83) compared with amoxicillin/clavulanate and 1.98 (95% CI = 1.41-2.8) compared with cefazolin and the RD was 0.118 (95% CI = 0.031-0.215) compared with amoxicillin/clavulanate and 0.131 (95% CI = 0.058-0.197) compared with cefazolin. We also estimated the NNH; replacing amoxicillin/clavulanate or cefazolin with cefuroxime would yield ceftazidime resistance in 1 more patient for every 8.5 (95% CI = 4.66-32.14) or 7.6 (95% CI = 5.1-17.3) patients re-hospitalized in the following year, respectively. CONCLUSIONS: Our results indicate that treatment with amoxicillin/clavulanate or cefazolin is preferable to cefuroxime, in terms of future collateral resistance. The results presented here are a first step towards quantitative estimations of the ecological damage caused by different antibiotics.

Revealing antibiotic cross-resistance patterns in hospitalized patients through Bayesian network modelling.

OBJECTIVES: Microbial resistance exhibits dependency patterns between different antibiotics, termed cross-resistance and collateral sensitivity. These patterns differ between experimental and clinical settings. It is unclear whether the differences result from biological reasons or from confounding, biasing results found in clinical settings. We set out to elucidate the underlying dependency patterns between resistance to different antibiotics from clinical data, while accounting for patient characteristics and previous antibiotic usage. METHODS: Additive Bayesian network modelling was employed to simultaneously estimate relationships between variables in a dataset of bacterial cultures derived from hospitalized patients and tested for resistance to multiple antibiotics. Data contained resistance results, patient demographics and previous antibiotic usage, for five bacterial species: Escherichia coli (n = 1054), Klebsiella pneumoniae (n = 664), Pseudomonas aeruginosa (n = 571), CoNS (n = 495) and Proteus mirabilis (n = 415). RESULTS: All links between resistance to the various antibiotics were positive. Multiple direct links between resistance of antibiotics from different classes were observed across bacterial species. For example, resistance to gentamicin in E. coli was directly linked with resistance to ciprofloxacin (OR = 8.39, 95% credible interval 5.58-13.30) and sulfamethoxazole/trimethoprim (OR = 2.95, 95% credible interval 1.97-4.51). In addition, resistance to various antibiotics was directly linked with previous antibiotic usage. CONCLUSIONS: Robust relationships among resistance to antibiotics belonging to different classes, as well as resistance being linked to having taken antibiotics of a different class, exist even when taking into account multiple covariate dependencies. These relationships could help inform choices of antibiotic treatment in clinical settings.