Relationships between patient- and institution-specific variables and decreased antimicrobial susceptibility of Gram-negative pathogens

Relationship between Fosfomycin Exposure and Amplification of Escherichia coli Subpopulations with Reduced Susceptibility in a Hollow-Fiber Infection Model

Understanding the relationship between antibiotic exposure and amplification of bacterial subpopulations with reduced drug susceptibility over time is important for evaluating the adequacy of dosing regimens. We utilized a hollow-fiber infection model to identify the fosfomycin intravenous dosing regimens that prevented the amplification of Escherichia coli bacterial subpopulations with reduced fosfomycin susceptibility. The challenge isolate was E. coli ATCC 25922 (agar MIC with glucose-6-phosphate, 1 mg/liter; agar MIC without glucose-6-phosphate, 32 mg/liter). The fosfomycin dosing regimens studied were 1 to 12 g every 8 h for 10 days to approximate that planned for clinical use. The studies included a no-treatment control regimen. Two bacterial subpopulations were identified, one with reduced susceptibility with agar MIC values ranging from 32 to 128 mg/liter and the other resistant with agar MIC values of 256 to >1,024 mg/liter on plates containing 5× and 256× the baseline MIC value, respectively. An inverted-U-shaped function best described the relationship between the amplification of the two bacterial subpopulations and drug exposure. The lowest fosfomycin dosing regimen that did not amplify a bacterial subpopulation with reduced susceptibility was 4 g administered every 8 h. Nearly immediate amplification of bacterial subpopulations with reduced susceptibility was observed with fosfomycin dosing regimens consisting of 1 to 2 g every 8 h. These data will be useful to support the selection of fosfomycin dosing regimens that minimize the potential for on-therapy amplification of bacterial subpopulations with reduced susceptibility.

Relationship between ceftolozane-tazobactam exposure and selection for Pseudomonas aeruginosa resistance in a hollow-fiber infection model

It is important to understand the relationship between antibiotic exposure and the selection of drug resistance in the context of therapy exposure. We sought to identify the ceftolozane-tazobactam exposure necessary to prevent the amplification of drug-resistant bacterial subpopulations in a hollow-fiber infection model. Two Pseudomonas aeruginosa challenge isolates were selected for study, a wild-type ATCC strain (ceftolozane-tazobactam MIC, 0.5 mg/liter) and a clinical isolate (ceftolozane-tazobactam MIC, 4 mg/liter). The experiment duration was 10 days, and the ceftolozane-tazobactam dose ratio (2:1) and dosing interval (every 8 h) were selected to approximate those expected to be used clinically. The studied ceftolozane-tazobactam dosing regimens ranged from 62.5/31.25 to 2,000/1,000 mg per dose in step fold dilutions. Negative-control arms included no treatment and tazobactam at 500 mg every 8 h. Positive-control arms included ceftolozane at 1 g every 8 h and piperacillin-tazobactam dosed at 4.5 g every 6 h. For the wild-type ATCC strain, resistance was not selected by any ceftolozane-tazobactam regimen evaluated. For the clinical isolate, an inverted-U-shaped function best described the relationship between the amplification of a drug-resistant subpopulation and drug exposure. The least (62.5/31.25 mg) and most (2,000/1,000 mg) intensive ceftolozane-tazobactam dosing regimens did not select for drug resistance. Drug resistance selection was observed with intermediately intensive dosing regimens (125/62.5 through 1,000/500 mg). For the intermediately intensive ceftolozane-tazobactam dosing regimens, the duration until the selection for drug resistance increased with dose regimen intensity. These data support the selection of ceftolozane-tazobactam dosing regimens that minimize the potential for on-therapy drug resistance selection.

Population biological principles of drug-resistance evolution in infectious diseases

The emergence of resistant pathogens in response to selection pressure by drugs and their possible disappearance when drug use is discontinued are evolutionary processes common to many pathogens. Population biological models have been used to study the dynamics of resistance in viruses, bacteria, and eukaryotic microparasites both at the level of the individual treated host and of the treated host population. Despite the existence of generic features that underlie such evolutionary dynamics, different conclusions have been reached about the key factors affecting the rate of resistance evolution and how to best use drugs to minimise the risk of generating high levels of resistance. Improved understanding of generic versus specific population biological aspects will help to translate results between different studies, and allow development of a more rational basis for sustainable drug use than exists at present.

On being the right size: the impact of population size and stochastic effects on the evolution of drug resistance in hospitals and the community

The evolution of drug resistant bacteria is a severe public health problem, both in hospitals and in the community. Currently, some countries aim at concentrating highly specialized services in large hospitals in order to improve patient outcomes. Emergent resistant strains often originate in health care facilities, but it is unknown to what extent hospital size affects resistance evolution and the resulting spillover of hospital-associated pathogens to the community. We used two published datasets from the US and Ireland to investigate the effects of hospital size and controlled for several confounders such as antimicrobial usage, sampling frequency, mortality, disinfection and length of stay. The proportion of patients acquiring both sensitive and resistant infections in a hospital strongly correlated with hospital size. Moreover, we observe the same pattern for both the percentage of resistant infections and the increase of hospital-acquired infections over time. One interpretation of this pattern is that chance effects in small hospitals impede the spread of drug-resistance. To investigate to what extent the size distribution of hospitals can directly affect the prevalence of antibiotic resistance, we use a stochastic epidemiological model describing the spread of drug resistance in a hospital setting as well as the interaction between one or several hospitals and the community. We show that the level of drug resistance typically increases with population size: In small hospitals chance effects cause large fluctuations in pathogen population size or even extinctions, both of which impede the acquisition and spread of drug resistance. Finally, we show that indirect transmission via environmental reservoirs can reduce the effect of hospital size because the slow turnover in the environment can prevent extinction of resistant strains. This implies that reducing environmental transmission is especially important in small hospitals, because such a reduction not only reduces overall transmission but might also facilitate the extinction of resistant strains. Overall, our study shows that the distribution of hospital sizes is a crucial factor for the spread of drug resistance.

The increasing spread of bacteria, which are resistant to antibiotics, is a serious threat to clinical care. Currently, several countries aim at concentrating highly specialized services in large hospitals in order to improve patient outcomes. However, empirical studies have shown that resistance levels correlate with hospital size. To illustrate this correlation, we analyze two published datasets from the US and Ireland and controlled for antimicrobial usage, disinfection and length of stay. The proportion of patients acquiring both sensitive and resistant infections in hospitals strongly correlated with hospital size. Moreover, we observe the same pattern for both the percentage of resistant infections and the temporal increase of hospital-acquired infections. To investigate to what extent hospital size can directly affect the prevalence of antibiotic resistance, we use mathematical models describing the epidemic spread of resistance in hospitals and the community. We find that small hospitals typically lead to considerably lower resistance levels than large hospitals. However, this beneficial effect of small hospital size may be reduced if bacteria are transmitted indirectly via the environment. Therefore, reducing environmental transmission might be particularly important in small hospitals. Overall, our findings suggest that the short-term benefits of larger hospitals may come at the price of increasing resistance in the long term.

Predictors of mortality in patients with bloodstream infection due to ceftazidime-resistant Klebsiella pneumoniae

Bloodstream infection (BSI) due to multidrug-resistant Klebsiella is associated with high rates of morbidity and mortality. The aim of this study was to identify predictors of in-hospital mortality among patients with BSI due to ceftazidime-resistant (CAZ-R) Klebsiella pneumoniae at a tertiary care medical center. Patients with CAZ-R K. pneumoniae BSI were identified by our microbiology laboratory between January 1995 and June 2003. Clinical data were collected retrospectively. Logistic regression was used to identify independent predictors of all causes of in-hospital mortality. Of 779 patients with K. pneumoniae BSI, 60 (7.7%) had BSI due to CAZ-R K. pneumoniae; 43 (72%) of these were nosocomial infections. Pulsed-field gel electrophoresis identified a single predominant strain in 17 (28%) patients. The in-hospital mortality rate was 43% (n = 26). Among patients with CAZ-R K. pneumoniae BSI, those who died were similar to survivors with respect to demographic, clinical, and antimicrobial susceptibility characteristics. Only 43 (72%) patients received effective therapy within 5 days of BSI. In bivariable analysis, delay in initiation of effective therapy for >72 h after diagnosis of BSI was associated with death (P = 0.03). Strain genotype was not predictive of outcome. In multivariable analysis, delay in initiation of effective therapy for >72 h after diagnosis of BSI was an independent predictor of death (odds ratio, 3.32; 95% confidence interval, 1.07 to 10.3). Thus, among patients with BSI due to CAZ-R K. pneumoniae, a delay in the initiation of effective therapy of greater than 72 h after BSI was associated with a >3-fold increase in mortality risk.

Comparison of censored regression and standard regression analyses for modeling relationships between antimicrobial susceptibility and patient- and institution-specific variables

In order to identify patients likely to be infected with resistant bacterial pathogens, analytic methods such as standard regression (SR) may be applied to surveillance data to determine patient- and institution-specific factors predictive of an increased MIC. However, the censored nature of MIC data (e.g., MIC < or = 0.5 mg/liter or MIC > 8 mg/liter) imposes certain limitations on the use of SR. In order to investigate the nature of these limitations, simulations were performed to compare a regression tailored for censored data (censored regression [CR]) and one tailored for an SR. By using a model relating piperacillin-tazobactam MICs against Enterobacter spp. to patient age and hospital bed capacity, 200 simulations of 500 isolates were performed. Various MIC censoring patterns were imposed by using 26 left- or right-censored (L,R) pairs (i.e., MICs < or = 2 mg/liter(L) [2L] or MICs > 2 mg/liter(R) [2R], respectively). Data were fit by CR and SR for which censored MICs were either (i) excluded, (ii) replaced by 2L or 2R, or (iii) replaced by 2(L - 1) or 2(R + 1). Total censoring for the 26 pairs ranged from 7 to 86%. By CR, deviations of average parameter estimates from the true parameter values were <0.10 log2 (mg/liter) for all parameters for each of the 26 pairs. By SR, these deviations were >0.10 log2 (mg/liter) for at least 18 of the 26 pairs for all but one parameter. Two-standard-error confidence intervals for individual parameters contained as little as 0% of cases for all SR approaches but > or = 91.5% of cases for the CR approach. When censored MIC data are modeled, CR may reduce or eliminate biased parameter estimates obtained by SR.

Relationship between increased levofloxacin use and decreased susceptibility of Streptococcus pneumoniae in the United States

Increasing reports of fluoroquinolone-non-susceptible Streptococcus pneumoniae are of clinical concern. We examined the relationship between outpatient fluoroquinolone use and susceptibility of community-acquired S. pneumoniae isolates. Using multivariable general linear modeling, US SENTRY Antimicrobial Surveillance Program and Intercontinental Medical Statistics data (1997-2002) were analyzed to determine the influence of selected patient-, institution-, and geographic region-specific factors, including local fluoroquinolone usage, on the minimum inhibitory concentration (MIC) of levofloxacin against S. pneumoniae. Levofloxacin MIC50, MIC90, and MIC range (n = 384 from 26 hospitals) were 1, 1, and < or =0.5 to >4 microg/mL, respectively. Variables associated with changes in geometric mean MIC included geographical region (P < 0.0001), medical service (P = 0.0002), study year (P = 0.0006), primary diagnosis group (P = 0.02), and 2 interactions (duration of hospital stay before isolate collection by bed capacity, P = 0.06, and levofloxacin use by geographical region, P = 0.08; P < 0.001 when study year was removed from the model). MIC increased with levofloxacin use across all geographical regions, with increases of 54% and 126% in the southwest and west, respectively. In contrast to other fluoroquinolones, increased levofloxacin use, along with other variables, was associated with decreased pneumococcal susceptibility. Given the US environment of increasing pneumococcal resistance, these data may be useful in better understanding factors related to emergence of fluoroquinolone resistance.

Replacement of broad-spectrum cephalosporins by piperacillin-tazobactam: impact on sustained high rates of bacterial resistance

We have previously observed a significant reduction of ceftriaxone resistance in Proteus mirabilis associated with an increase in the use of cefepime, along with a decrease in the consumption of broad-spectrum cephalosporins (CEP). However, we did not observe such a reduction with Klebsiella pneumoniae. Therefore, we sought to determine whether replacement of CEP by piperacillin-tazobactam might be useful in reducing sustained high rates of CEP resistance by this organism. We used a 6-month "before and after model"; during the second (intervention) period, most prescriptions of CEP were changed to piperacillin-tazobactam at the pharmacy. No additional barrier precautions were undertaken. During intervention, consumption of ceftazidime decreased from 17.73 to 1.14 defined daily doses (DDD) per 1,000 patient-days (P < 0.0001), whereas that of piperacillin-tazobactam increased from 0 to 30.57 DDD per 1,000 patient-days (P < 0.0001). The levels of resistance to CEP by K. pneumoniae and P. mirabilis decreased from 68.4 and 57.9% to 37.5 and 29.4%, respectively (P < 0.05). We conclude that replacement of ceftazidime by piperacillin-tazobactam might be a suitable strategy to decrease endemic CEP resistance by K. pneumoniae and P. mirabilis, even where there are high bacterial resistance rates and irrespective of any additional precautions for controlling nosocomial infection.