Recent exposure to azoles is an important risk factor for infection with fluconazole-resistant Candida spp., but little is known about the role of antibacterial drug exposure in the emergence of drug-resistant Candida. We did a prospective nationwide surveillance study of candidemia in Israel and analyzed the propensity score-adjusted association between antifungal and antibacterial drug exposure and bloodstream infection with C. glabrata and fluconazole-resistant Candida isolates. Four hundred forty-four episodes of candidemia (450 Candida isolates, 69 [15%] C. glabrata isolates, and 38 [8.5%] fluconazole-resistant isolates) from 18 medical centers in Israel were included. C. glabrata bloodstream infection was strongly associated with recent metronidazole exposure (odds ratio [OR], 3.2; P < 0.001). Infection with a fluconazole-resistant isolate was associated with exposure to carbapenems, trimethoprim-sulfamethoxazole, clindamycin, and colistin (odds ratio, 2.8; P = 0.01). The inclusion of antibacterial drug exposure in a multivariable model significantly enhanced the model's predictive accuracy for fluconazole-resistant Candida bloodstream infection. Our findings may be relevant to the selection of empirical antifungal treatment and broaden the scope of antibiotic-associated collateral damage.


Candida species have emerged as frequent causes of nosocomial bloodstream infection (BSI) in association with well-defined risk factors, including prolonged hospitalization, abdominal surgery, antibiotic treatment, neutropenia and central venous catheterization (14). Candidemia is associated with high rates of attributable mortality, prolongation of hospital stay, and excessive costs (28). In recent years, there has been a shift in the distribution of Candida species causing invasive infection, with non-albicans species now surpassing Candida albicans in many institutions (14, 25). Of particular concern is the rising incidence of the azole-nonsusceptible species C. glabrata and the inherently fluconazole-resistant species C. krusei (11, 25, 27).
Fluconazole is often used as empirical treatment of candidemia. However, given the correlation between the survival rate and the timely initiation of appropriate treatment for candidemia (8), accurate assessment of the risk of fluconazole-resistant Candida (FRC) BSI is of prime importance. Patients who were recently treated with an azole drug are at increased risk of infection with FRC (9) and should be treated initially with an echinocandin agent according to current guidelines (18). However, experimental and clinical data support the notion that nonantifungal antimicrobial agents also affect the risk of colonization and infection with FRC (15, 17, 22). Since exposure to antibacterial drugs among at-risk patients far exceeds exposure to antifungal agents, even modest effects of individual antibacterials could translate into significant overall changes in the susceptibility patterns of Candida spp. Nevertheless, the collateral effects of antibacterial drugs on Candida spp. are poorly understood. To address this question, we analyzed prospectively collected data from a nationwide study of candidemia in Israel and examined the association between exposure to antifungal and antibacterial agents and the risk of infection with FRC.
(Presented in part at the 50th Interscience Conference on Antimicrobial Agents and Chemotherapy, Boston, MA [abstract M-1068]).


Study design.

We performed a prospective nationwide study of candidemia in Israel from November 2005 through June 2007. Eighteen medical centers, which together account for 75% of the hospital beds in Israel, were included. All candidemia episodes that occurred in the participating centers during the study period were eligible for inclusion in this study. Clinical data were prospectively entered into standardized data forms by on-site investigators at each of the centers. The Candida sp. clinical isolates underwent preliminary identification and susceptibility testing in each center according to local practices. Subsequently, the isolates were transferred together with the corresponding data forms to the central study site, where species identification and susceptibility testing were performed as detailed below. The data forms were collected by the study coordinator, reviewed by the principal investigator, and entered into a computerized database. The study was approved by the ethics committee of each of the participating centers.

Data collection.

On-site data collection included demographics, performance status, Charlson comorbidity index (4), and the presence of any of the following conditions in the month preceding candidemia: surgery, hematopoietic stem cell or solid-organ transplantation, cytotoxic chemotherapy, systemic corticosteroid treatment (a dose equivalent to prednisone at ≥10 mg/day for at least 14 days), neutropenia (an absolute neutrophil count of <500 cells/μl), indwelling central vascular catheter, urinary bladder catheter, intravenous drug abuse, prematurity, intensive care unit hospitalization, mechanical ventilation, burns, or dialysis. In addition, a detailed history of antifungal and antibacterial drug use in the month preceding candidemia was obtained.

Microbiological testing.

Species identification and susceptibility testing were performed at the central study site. All fungal isolates were maintained in sterile water at −80°C until testing. Prior to testing, each strain was passaged on Sabouraud's dextrose agar to ensure purity and viability. Candida species were identified using standard microbiology methods, including growth on Chromagar Candida (Chromagar, Paris, France) and the Vitek 2 system with use of the YST-ID card (bioMérieux, Durham, NC). Susceptibility to fluconazole was determined using the Etest (AB Biodisk, Sweden) method according to the manufacturer's instructions. Susceptibility results were interpreted according to the recently revised Clinical and Laboratory Standards Institute breakpoints for fluconazole (21). Specifically, for all Candida species except C. glabrata and C. krusei, fluconazole MIC breakpoints were as follows: susceptible, ≤2 μg/ml; susceptible dose-dependent, 4 to 8 μg/ml; and resistant, >8 μg/ml. For C. glabrata, the corresponding MIC breakpoints were <8 μg/ml, 16 to 32 μg/ml, and >32 μg/ml, respectively. C. krusei was considered always resistant to fluconazole. Susceptibility testing was performed at least in duplicate for each isolate, and the highest MIC was reported.

Statistical analyses.

To identify predictors of FRC BSI, we first performed bivariable analyses using chi-square and Fisher's exact tests for categorical variables and Student's t test for continuous variables. The variables were then tested in a multivariable logistic regression model. The variables were added individually to the regression model to confirm their association with FRC BSI. Next, the simultaneous effects of variables that were significantly associated with FRC BSI individually were modeled. The significance threshold for retaining variables in the model was a P value of <0.05. Goodness of fit for multivariable models was assessed with the Hosmer Lameshow test, and predictive accuracy was assessed by calculating the area under the receiver-operator characteristics (ROC) curve.
The effect of antimicrobial drug exposure was analyzed for each drug separately, as well as for antimicrobial drug categories (β-lactams, penicillins, cephalosporins, carbapenems, aminoglycosides, fluoroquinolones, macrolides, tetracyclines, and triazoles) (see Table 2). Exposure to antibacterials with antianaerobic activity (metronidazole, clindamycin, carbapenems, β-lactam/β-lactamase inhibitor combinations, and chloramphenicol) was also analyzed in aggregate.
To limit confounding by nonantibacterial risk factors, we calculated the conditional probability of recent exposure to specific antibacterial drugs based on nonantibacterial risk factors using propensity score analysis (23, 24). Propensity scores were generated using logistic regression, with antibacterial drug exposure as the dependent variable. Nonantibacterial covariates were included in the multivariable model by stepwise selection, with a P value of <0.05 set as the limit for inclusion in the model. We tested whether the balancing property of the propensity score was satisfied by subclassification of the cohort into quintiles based on individual propensity scores. Then, using FRC BSI as the outcome variable, individual antibacterial drugs and drug classes were analyzed using logistic regression adjusted for the propensity score and the number of days at risk. Calculations were performed with the Stata software package (version 11.1; StataCorp, College Station, TX).


A total of 450 patient-specific Candida sp. bloodstream isolates from 444 patients were included in this study. Patient demographics and clinical risk factors for candidemia are summarized in Table 1. The majority of candidemia episodes (97.8%) were nosocomial; 355 (80%) occurred in hospitalized patients, and 79 (17.8%) occurred in outpatients discharged from the hospital within the previous 30 days and were therefore considered health care associated. C. albicans was the most frequent species (198 cases; 44.5%), followed by C. parapsilosis (n = 75; 16.8%), C. tropicalis (n = 74; 16.6%), and C. glabrata (n = 68; 15.3%).
Table 1
Table 1 Demographic and clinical features of 444 patients with Candida bloodstream infection
    Male238 (53.6)
    Female206 (46.4)
Age (yr)65 (43–87)
    ≤1 yr52 (11.7)
    ≥65 yr224 (50.5)
Residence at a long-term care facility48 (10.8)
Performance status 
    Independent191 (43.0)
    Partially dependent88 (19.8)
    Completely dependent89 (20.0)
    Unknown76 (17.1)
Charlson score3 (1–5)
Exposure to candidemia risk factorsa 
    Antibiotic use410 (92.3)
    Central vascular catheter331 (74.5)
    Urinary bladder catheter245 (55.1)
    Parenteral nutritional support147 (33.1)
    Stay at an intensive-care unit197 (44.4)
    Mechanical ventilation199 (44.8)
    Surgery175 (39.4)
        Abdominal87 (19.6)
        Chest26 (5.9)
        Other92 (20.7)
    Cytotoxic chemotherapy82 (18.5)
    Neutropeniab54 (12.2)
    Dialysis39 (8.7)
    Prematurity29 (6.5)
    Stem cell transplantation22 (5.0)
    Burns11 (2.5)
    Systemic corticosteroidsc7 (1.6)
    Intravenous drug abuse7 (1.6)
    Solid-organ transplantation4 (0.9)
Severity of illness and outcome 
    Shock70 (15.7)
    Renal failure35 (7.8)
    Respiratory failure40 (9.0)
    In-hospital death216 (48.7)
Within 30 days prior to the onset of candidemia.
Absolute neutrophil count of <500 cells/μl.
Defined as use of a systemic corticosteroid at a dose equivalent to prednisone at ≥10 mg/day for at least 14 days within the month preceding candidemia.
All n (%), except age and Charlson score, which are median (interquartile range).

Antimicrobial drug exposure.

Of 444 patients in the study cohort, 410 (92.3%) received treatment with at least one antibacterial agent within 30 days prior to the onset of candidemia. The most common antibacterial agents were β-lactams (88%), vancomycin (44%), aminoglycosides (31%), and metronidazole (29%) (Table 2). Most patients (359; 81%) were exposed to multiple antibacterial drugs, either concomitantly or sequentially. Patients received a median of 3 antibacterial drugs (interquartile range, 2 to 4) in the month preceding candidemia. Sixty-three patients (14%) had received a systemic antifungal agent within 30 days prior to candidemia, most commonly fluconazole (56 patients) or amphotericin B (8 patients).
Table 2
Table 2 Unadjusted bivariate associations between antimicrobial drug exposure and Candida sp. infection in 444 patient-specific episodes of bloodstream infection
Antimicrobial agentn (%)C. glabrata (n = 68 [(15.3%])Fluconazole-resistant Candida spp.a (n = 38 [8.5%])
Odds ratio (95% CI)POdds ratio (95% CI)P
All antibacterial drugsh410 (92)    
    β-Lactamb391 (88)1.0 (0.4–2.6)0.92.5 (0.6–22.7)0.1
    Penicillinc262 (59)0.9 (0.5–1.7)0.81.7 (0.8–4.0)0.1
    β-Lactam/β-lactamase inhibitord200 (44)0.8 (0.4–1.5)0.61.4 (0.7–3.0)0.2
    Cephalosporin240 (54)1.0 (0.5–1.7)0.80.4 (0.1–0.8)0.01
    Carbapenem152 (34)0.6 (0.3–1.1)0.22.3 (1.1–4.7)0.01
    Fluoroquinolone94 (21)0.9 (0.4–1.8)0.81.3 (0.5–3.0)0.4
    Metronidazole130 (29)2.7 (1.5–4.7)i<0.0011.2 (0.5–2.7)0.4
    Clindamycin12 (2.7)1.8 (0.5–6.6)0.33.7 (1.06–13.6)0.03
    Trimethoprim-sulfamethoxazole22 (4.9)0.8 (0.1–3.0)0.84.5 (1.3–13.3)0.001
    Macrolide33 (7.4)1.2 (0.4–3.2)0.61.0 (0.1–3.7)0.9
    Vancomycin196 (44)0.6 (0.3–1.06)0.061.2 (0.6–2.6)0.4
    Aminoglycoside140 (31)0.4 (0.2–0.9)0.011.0 (0.4–2.1)0.9
    Colistin31 (6.9)1.0 (0.4–2.7)0.82.8 (1.1–7.2)0.02
    Antianaerobic agentse238 (53)1.4 (0.6–2.5)0.42.1 (0.8–6.3)0.09
All antifungal drugsf63 (14)1.0 (0.4–2.2)0.84.8 (2.1–10.4)<0.0001
    Amphotericin B8 (1.8)0.7 (0.01–6.2)0.83.7 (0.3–21.6)0.09
    Fluconazole56 (13)1.2 (0.5–2.6)0.65.0 (2.2–11.0)<0.0001
    Itraconazole3 (0.7)2.7 (0.04–54)0.3NAg<0.0001
    Voriconazole3 (0.7)0 (0–7.1)0.45.4 (0.09–106.4)0.1
    Any triazole61 (14)1.1 (0.4–2.4)0.75.3 (2.4–11.6)<0.0001
The fluconazole-resistant strains were C. krusei (n = 14), C. parapsilosis (n = 10), C. glabrata (n = 6), C. tropicalis (n = 5), C. guilliermondii (n = 2), and C. farinosa (n = 1).
Includes penicillins, cephalosporins, and carbapenems.
Includes penicillin G, penicillin VK, amoxicillin, ampicillin, and cloxacillin.
Includes amoxicillin-clavulanic acid, ampicillin-sulbactam, and piperacillin-tazobactam.
Aggregate of antimicrobial agents with antianaerobic activity; includes metronidazole, clindamycin, carbapenems, and β-lactam/β-lactamase inhibitor combinations.
There were no cases of candidemia in patients with exposure to echinocandins within the previous month.
NA, not applicable, i.e., cannot be calculated because all 3 patients with itraconazole exposure had FRC BSI.
Not shown in the table are antibacterial agents that were given to small numbers of patients: rifampin (8 patients), linezolid (6 patients), tetracyclines (5 patients), and nitrofurantoin (2 patients).
Boldface indicates statistically significant associations.

C. glabrata BSI.

There were 68 episodes of C. glabrata BSI. Bivariable analysis identified a positive association of C. glabrata infection with metronidazole exposure and a negative association with aminoglycoside exposure (Table 2). Nonantibiotic predictors of C. glabrata BSI were an age of ≥65 years, poor performance status, an indwelling urinary bladder catheter, residence at a long-term care facility, and a Charlson score of ≥1. Neutropenia and the presence of a central venous catheter were negatively associated with C. glabrata infection (Fig. 1A).
Fig 1
Fig 1 Association of antibiotic and nonantibiotic covariates with C. glabrata, and fluconazole-resistant Candida bloodstream infection. The forest plots show the associations of antibiotic and nonantibiotic covariates with C. glabrata BSI (A) and FRC BSI (B). The individual graphs show significantly associated covariates by bivariable analysis and multivariable analysis. The plots show the odds ratio (symbol) and 95% confidence interval (whiskers) for each covariate. Solid symbols are used for nonantibacterial covariates and open symbols for antibacterial covariates. All covariates refer to exposure within 30 days prior to the onset of candidemia. CVC, central venous catheter. Antibacterial drug exposure denotes exposure to carbapenems, trimethoprim-sulfamethoxazole, colistin, or clindamycin.
On multivariable analysis, recent metronidazole exposure remained a significant predictor of C. glabrata infection (adjusted odds ratio [OR], 3.2; 95% confidence interval [CI], 1.7 to 6.0; P < 0.001), together with poor performance status (OR, 1.8; P = 0.04), neutropenia (OR, 0.1; P = 0.03), and the presence of a central venous catheter (OR, 0.4; P = 0.02) (Fig. 1A).

Fluconazole-resistant Candida sp. BSI.

Fifty-four episodes of candidemia (12.1%) were caused by isolates nonsusceptible to fluconazole: 16 (3.6%) were susceptible dose dependent, and 38 (8.5%) were resistant to fluconazole. The 38 fluconazole-resistant bloodstream isolates were C. krusei (14 of 14 isolates), C. parapsilosis (10/75; 13.3%), C. glabrata (6/68; 8.8%), C. tropicalis (5/74; 6.7%), C. guilliermondii (2/2), and C. farinosa (1/1).
Bivariable analysis revealed a significant association between FRC BSI and exposure to trimethoprim-sulfamethoxazole (TMP-SMX) (OR, 4.5; P = 0.001), carbapenems (OR, 2.3; P = 0.01), clindamycin (OR, 3.7; P = 0.03), and colistin (OR, 2.8; P = 0.02). Exposure to cephalosporins was negatively associated with FRC BSI (OR, 0.4; P = 0.01) (Table 2 and Fig. 1). Exposure to antianaerobic antibiotics was associated with a non-statistically significant trend for FRC BSI (OR, 2.1; P = 0.09).
As described in Materials and Methods, we constructed a propensity score that predicted a patient's likelihood of receiving any of the four antibacterial drugs associated with increased risk of FRC BSI. The nonantibacterial covariates ultimately included in the propensity score are shown in Table 3. Importantly, indices of the severity of illness at the time of candidemia (circulatory shock, renal failure, and respiratory failure) were not associated with the risk of exposure to one of these antibacterial agents. In the propensity-adjusted multivariable analysis, FRC BSI remained significantly associated with exposure to one of the four antibacterial drug classes (OR, 2.8; 95% CI, 1.2 to 6.3; P = 0.01), together with neutropenia (OR, 3.3; 95% CI, 1.5 to 7.3; P = 0.002) and recent fluconazole exposure (OR, 4.3; 95% CI, 1.5 to 12.2; P = 0.005) (Fig. 1).
Table 3
Table 3 Risk factors for exposure to a high-risk antibacterial druga used to calculate the propensity score
Risk factorOdds ratio (95% confidence interval)P value
Urinary bladder catheter2.2 (1.4–3.4)<0.0001
Hematopoietic stem cell transplantation3.6 (1.3–9.6)0.009
Recent azole exposure2.3 (1.2–4.3)0.007
Time at risk1.01 (1.008–1.02)<0.0001
High-risk antibacterial drugs were trimethoprim-sulfamethoxazole, carbapenems, clindamycin, and colistin.
To assess whether obtaining a history of recent antibacterial drug exposure can enhance the accuracy of predictive models to detect FRC BSI, we determined the incremental effect of antibacterial covariates on the area under the ROC curve. We compared the performances of three models; all included neutropenia as a covariate, together with previous fluconazole exposure (model 1), exposure to antibacterial drugs (carbapenems, trimethoprim-sulfamethoxazole, clindamycin, or colistin) (model 2), and exposure to fluconazole and antibacterials (model 3). The predictive accuracy for FRC BSI, expressed as the area under the ROC curve, was 0.67, 0.76, and 0.78 for models 1, 2, and 3, respectively, and was significantly greater for models that included antibacterial exposure (models 2 and 3) than for the model that included only neutropenia and fluconazole exposure (model 1) (P = 0.003) (Fig. 2).
Fig 2
Fig 2 Comparative accuracy of predictive models for fluconazole-resistant Candida sp. bloodstream infection. The predictive accuracy, as represented by the area under the ROC plot, is shown for 3 models. FRC BSI is the dependent variable for all models. Covariates for model 1 were neutropenia and exposure to fluconazole; those for model 2 were neutropenia and exposure to antibacterial drugs (carbapenems, trimethoprim-sulfamethoxazole, clindamycin, or colistin); those for model 3 were neutropenia, exposure to fluconazole, and exposure to antibacterials. The area under the ROC curve was significantly higher for models that included antibacterial covariates (2 and 3) than for model 1 (P = 0.003).


In this analysis of data from a national candidemia study, we found that recent exposure to antibacterial drugs affected the risk of bloodstream infection with fluconazole-resistant Candida isolates. Moreover, inclusion of antibacterial drugs in a multivariable model enhanced the model's predictive accuracy for fluconazole resistance compared to a model based on neutropenia and azole exposure alone. These findings suggest that “collateral damage,” a term used to describe the adverse ecological effects of antibacterial drug use (19), extends beyond the selection of drug resistance among bacteria and that antibiotic pressure may have significant effects on azole resistance in Candida spp.
At least four potential mechanisms may underlie the observed associations between antibacterial drug exposure and candidemia. First, by altering the resident gut flora, antibacterials may selectively impair colonization resistance in a way that favors gastrointestinal colonization with drug-resistant Candida species. Colonization of the gut with Candida spp. is an antecedent to hematogenous dissemination in both immunocompetent and neutropenic individuals (5). Specifically, antibacterial drugs with predominant effects on anaerobic bacteria, such as metronidazole and clindamycin, were shown to promote intestinal colonization by C. glabrata in an animal model (22). In another study, the addition of metronidazole to a gastrointestinal decontamination regimen that included ciprofloxacin and fluconazole increased intestinal yeast colonization (26). Second, many antibacterial agents have some degree of antifungal activity (1), which could explain selective pressure similar to that induced by azole exposure. Metronidazole is an imidazole derivative with weak in vitro activity against Candida spp. but additive or synergistic fungicidal activity when combined with amphotericin B (3, 6). TMP-SMX and the polymyxins display in vitro activity against a variety of fungal organisms, including Candida spp. (2, 30). Third, some antibacterials directly modulate azole resistance by inducing the expression of efflux pump-encoding genes (13). Lastly, the immunomodulatory effects of antibacterial drugs might predispose for certain fungal pathogens. For example, sulfonamides were shown to have both inhibitory and stimulatory effects on the host response against Candida spp. (7, 16), whereas fluoroquinolones had no effect at therapeutic concentrations (10).
A number of case-control studies have reported exposure to antibacterial drugs with an antianaerobic spectrum of activity as a risk factor for candidemia (29), and more specifically for C. glabrata BSI (15, 17). Similar to our findings, Lee at al. reported that metronidazole use was associated with fluconazole-susceptible C. glabrata BSI, but not with fluconazole-resistant C. glabrata BSI (15). Interestingly, 3 of the 5 antibacterial drugs linked with fluconazole-resistant isolates in our study (metronidazole, clindamycin, and carbapenems) have significant antianaerobic activity.
A striking feature of the current cohort of patients with candidemia is the almost universal exposure to antibacterial drugs in the preceding month. Moreover, the majority of patients received multiple classes of antibacterials, either concomitantly or sequentially. These findings underscore the importance of addressing the antibacterial burden, which in a hospitalized population frequently constitutes the sum of multiple drug effects.
The limitations of our study are inherent in its observational nature. Exposure to antibacterial drugs may reflect several confounding covariates, such as severity of illness, length of hospitalization, and comorbid conditions (confounding by indication). In our patient cohort, there was no significant association between the occurrence of FRC BSI or exposure to the antibacterials of interest and severity of illness. We sought to adjust for possible confounders using multivariable analyses and propensity score adjustment. Propensity score matching aims to balance confounding covariates between antibiotic-treated and untreated patients. Importantly, we adjusted all risk estimates for the number of days at risk. However, even this methodology cannot correct for unknown confounders. In addition, it should be noted that the rate of fluconazole resistance in C. glabrata isolates was lower than that reported for most populations (20). Different antibacterial drugs may affect fluconazole resistance in populations where higher C. glabrata resistance rates are observed. Thus, our predictive model should be validated for different patient cohorts. Of note, we used the recently adjusted CLSI clinical breakpoints for fluconazole and Candida susceptibility, which should increase the sensitivity of detecting emerging resistance in common Candida sp. isolates (21). Compared with previous CLSI breakpoints, use of the current values increased the rate of fluconazole resistance in Candida bloodstream isolates from 5.3% to 8.5%, with the most marked increase occurring in C. parapsilosis (1.3% to 13.3%).
Unnecessary use of antibiotics is frequent, accounting for as much as 30% of total antimicrobial therapy days, with antianaerobic agents accounting for a third of redundant antibacterial drug use (12). It is now well recognized that antibacterial drugs promote the emergence and dissemination of multidrug-resistant nosocomial bacteria in a class-specific manner (19). Selection of fluconazole-resistant invasive Candida strains may represent an additional adverse consequence of excessive antibiotic use. Recognizing robust associations between antibacterial drug exposure and FRC BSI should allow the implementation of improved predictive schemes to direct empirical antifungal treatment in high-risk patients.


This work was funded in part by a grant from Merck Sharp & Dohme Ltd., Israel.
We thank Anna Novikov and Nathaniel Albert for excellent technical assistance and Esther Shabtai for statistical consultation.
Additional Israeli Candidemia Study Group Invesigators: Ruth Orni-Wasserlauf, Tel Aviv Sourasky Medical Center, Tel Aviv; Allon Moses and Hila Elinav, Hadassah Medical Center, Jerusalem; Galia Rahav, Yasmin Maor, and Anna Goldshmidt-Reuven, Chaim Sheba Medical Center, Tel Hashomer; Renato Finkelstein and Hannah Sprecher, Rambam Medical Center, Haifa; Michal Paul, Itzhak Levi, Zmira Samra, and Elad Goldberg, Rabin Medical Center and Schneider Children's Hospital, Petah Tikva; Tamar Gottesman and Orna Schwartz-Harari, Wolfson Medical Center, Holon; Amos Yinon and Orli Megged, Shaare Zedek Medical Center, Jerusalem; Tsilia Lazarovitch, Assaf Harofeh Medical Center, Zerifin; Pnina Ciobotaro, Kaplan Medical Center, Rehovot; BatSheva Gottesman, Meir Medical Center, Kfar Saba; Alona Paz, Bnai Zion Medical Center, Haifa; Raul Raz and Dan Miron, Emek Medical Center, Afula; Klaris Riesenberg and Lisa Saidel, Soroka Medical Center, Beer Sheva; Efraim Halperin, Bikur Holim Medical Center, Jerusalem; David Hassin, Hillel Yafe Medical Center, Hadera; and Soboh Soboh, Poriya Medical Center, Tiberias.


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Information & Contributors


Published In

cover image Antimicrobial Agents and Chemotherapy
Antimicrobial Agents and Chemotherapy
Volume 56Number 5May 2012
Pages: 2518 - 2523
PubMed: 22314534


Received: 14 October 2011
Returned for modification: 5 December 2011
Accepted: 31 January 2012
Published online: 12 April 2012


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Ronen Ben-Ami
Tel Aviv Sourasky Medical Center, Tel Aviv
Keren Olshtain-Pops
Hadassah-Hebrew University Medical Center, Jerusalem
Michal Krieger
Chaim Sheba Medical Center, Tel Hashomer
Ilana Oren
Rambam Medical Center, Haifa
Jihad Bishara
Rabin Medical Center and Schneider Children's Hospital, Petah Tikva
Michael Dan
Wolfson Medical Center, Holon
Yonit Wiener-Well
Shaare Zedek Medical Center, Jerusalem
Miriam Weinberger
Assaf Harofeh Medical Center, Zerifin
Oren Zimhony
Kaplan Medical Center, Rehovot
Michal Chowers
Meir Medical Center, Kfar Saba
Gabriel Weber
Carmel Medical Center, Haifa
Israel Potasman
Bnai Zion Medical Center, Haifa
Bibiana Chazan
Emek Medical Center, Afula, and Ziv Medical Center, Zefat, Israel
Imad Kassis
Rambam Medical Center, Haifa
Itamar Shalit
Rabin Medical Center and Schneider Children's Hospital, Petah Tikva
Colin Block
Hadassah-Hebrew University Medical Center, Jerusalem
Nathan Keller
Chaim Sheba Medical Center, Tel Hashomer
Dimitrios P. Kontoyiannis
M. D. Anderson Cancer Center, Houston, Texas, USA
Michael Giladi
Tel Aviv Sourasky Medical Center, Tel Aviv
for the Israeli Candidemia Study Group


Address correspondence to Ronen Ben-Ami, [email protected].

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