• We are sorry, but NCBI web applications do not support your browser and may not function properly. More information
Logo of aacPermissionsJournals.ASM.orgJournalAAC ArticleJournal InfoAuthorsReviewers
Antimicrob Agents Chemother. Mar 2007; 51(3): 839–844.
Published online Dec 28, 2006. doi:  10.1128/AAC.00901-06
PMCID: PMC1803143

Impact of Empiric Antibiotic Therapy on Outcomes in Patients with Pseudomonas aeruginosa Bacteremia[down-pointing small open triangle]


The impact of appropriate empirical antimicrobial therapy for Pseudomonas aeruginosa bacteremia on patient outcomes has not been clearly established. We assessed the effect of appropriate empirical therapy on in-hospital mortality and length of stay (LOS) among patients with P. aeruginosa bacteremia. This was a retrospective cohort study of inpatients with a positive blood culture for P. aeruginosa between January 2001 and June 2005. Empirical therapy was defined as appropriate if the patient received an antibiotic the organism was susceptible to between 8 h before culture collection and the time the susceptibility results were available. The severity of the illness was measured 24 h before culture collection. The data were analyzed using logistic regression (in-hospital mortality) and linear regression (LOS). Overall, there were 167 episodes of P. aeruginosa bacteremia, 123 (86%) of which received appropriate empirical antibiotics. Sixty-one patients died (36.5%). The median time from culture collection to susceptibility results was 3.4 days. After we adjusted for age, severity of illness, and time at risk, we found that the appropriate empirical therapy was not significantly associated with mortality (odds ratio = 0.96; 95% confidence interval = 0.31 to 2.93). There was a 7% reduction in the mean LOS for patients who had received appropriate therapy at the time susceptibility results were available compared to those who did not (P = 0.74). These data suggest that the use of appropriate empirical therapy, i.e., before susceptibility results are known may not be as critical to patient outcomes as other studies have suggested.

Despite the advent of potent antibiotics and improvement in supportive care, Pseudomonas aeruginosa bacteremia remains one of the most serious hospital-acquired infections, with a case-specific mortality ranging from 18 to 39% (1, 2, 4, 14, 31). This high mortality may be attributable to the inherent virulence of the organism, as well as to the fact that it often occurs in patients with immunosuppression or comorbidities such as neutropenia and underlying malignancy (8, 14). In addition, P. aeruginosa is susceptible to a limited number of antimicrobial agents, which increases the likelihood of inappropriate empirical antimicrobial therapy.

In general, it is reasonable to assume that the administration of antibiotics directed at the causative pathogen as early as possible in the course of infection would improve patient outcomes. This principle often triggers physicians to use broad-spectrum antipseudomonal agents in cases of presumed sepsis, which may lead to overuse of these drugs, and a subsequent increase in adverse events, increased costs, and antimicrobial resistance. It is therefore important to ensure that this principle is based on a true association. However, this hypothesis has not been clearly verified in P. aeruginosa bacteremia. Although several studies have found a distinct association between adequate empirical therapy and outcomes in cases of P. aeruginosa bacteremia (1, 3, 25), others have suggested that such associations do not exist (2, 13, 22, 23).

These studies are, by necessity, observational in nature, and therefore adequately controlling for confounding variables is more difficult. In addition, methodological differences in the way these studies were conducted may explain the conflicting results. First, the term “empirical therapy” is not defined consistently across the literature, and the time at which empirical therapy was assessed varied. Second, aggregate scores used to control for severity of illness were often measured at a time when they did not accurately reflect a patient's baseline risk of mortality. Third, many studies on this subject were designed to evaluate the outcomes of bacteremias caused by gram-negative organisms in general and thus had smaller numbers of patients with P. aeruginosa bacteremia, thereby decreasing their statistical power for this subgroup.

Our study sought to evaluate the effect of inappropriate empirical therapy at three distinct time points on mortality and length of stay among patients with P. aeruginosa bacteremia, while controlling for severity of illness before the onset of the bacteremia.


Study location, design, and data collection.

We performed a retrospective cohort analysis of all adult patients admitted to the University of Maryland Medical Center (UMMC) who had a positive blood culture for P. aeruginosa between 1 January 2001 and 30 June 2005 and who had a complete medication administration record available for review. We identified eligible patients using the UMMC central data repository, a relational database that contains patient medical and administrative data. This repository was also used to collect administrative, pharmacy, laboratory and outcomes data for all patients in the cohort. The data contained within the tables of the repository have been validated against medical records between October 1997 and May 2005 for previous research studies (6, 9-11, 18, 24). For a sample of the data, additional variables, such as the laboratory results included in the Acute Physiology and Chronic Health Evaluation III (APACHE III) score and the time at which the antibiotic susceptibility tests were available, were validated against patient records during the present study and found to be 100% reliable. Patients with a polymicrobial bacteremia were included in the present study. However, patients who experienced multiple episodes of bacteremia during the study period were allowed to enter the cohort multiple times only if the bacteremias took place during separate hospital admissions.

Variables electronically collected included age, gender, blood culture source, date and time the blood culture of interest was obtained, patient location at the time of blood culture, length of stay (index blood culture collection date to hospital discharge date), time prior to blood culture collection (hospital admission date to index blood culture collection date), time to susceptibility results (index blood culture collection date to date of receipt of antibiotic susceptibility testing results), and susceptibility results for the organism. We identified all coinfecting species and their resistance profile for patients who had more than one organism cultured out of the index blood culture (polymicrobial bacteremias).

Medication administration records for each patient were examined to determine whether and when the patient received appropriate empirical antibiotics. Additional information such as vital signs, the presence of a ventilator at admission or at culture collection, and data about central lines were also collected from patient records.

All microbiological data were collected retrospectively through the UMMC central data repository, and all blood cultures and susceptibility tests were performed by the UMMC clinical microbiology laboratory as part of the patients' standard care, according to the recommendations of the Clinical and Laboratory Standards Institute (formerly the National Committee for Clinical Laboratory Standards) (26).

Variable definitions.

Empirical antibiotic therapy was defined as the antibiotic therapy received by the patient between 8 h before the index blood culture was drawn and the time antibiotic susceptibility testing results were available. Empirical antibiotic therapy was considered appropriate if it included antipseudomonal antibiotics to which the specific Pseudomonas isolate displayed in vitro susceptibility. Although aztreonam may be effective for P. aeruginosa bloodstream infections, neither aminoglycoside nor aztreonam monotherapy were considered adequate therapy for P. aeruginosa bacteremia in the present study and were classified in the inappropriate group (12, 27). To more completely evaluate the impact of empirical therapy, we assessed adequate therapy in three distinct windows: between 8 h before the time the culture was obtained and 24 h afterward, between 24 and 48 h after the culture was obtained, and from 48 h after the culture was obtained to 4 h after the time the antibiotic susceptibility testing results were available from the UMMC microbiology laboratory. Susceptibility results are updated automatically in the electronic patient record available to the clinician as soon as they are uploaded onto the laboratory database by the technician. The prior positive blood culture result then gets flagged as a new result, prompting the clinician to access the newly updated information. Patients who received appropriate therapy during one time period were considered to have received appropriate therapy for all subsequent time periods. For example, if a patient started receiving appropriate therapy between 24 h and 48 h after the index blood culture was obtained, this patient was considered to be on appropriate therapy at the subsequent time-period (from 48 h to receipt of susceptibility results) as well.

We collected data on the presence and removal of central lines. Central lines were defined as all central venous catheters, peripherally inserted central catheters (PICC lines), dialysis catheters, and medication ports. The nursing flow sheets were reviewed to identify the presence of a central line at the time of culture collection and at 24 and 48 h after that time point. Multiple lines at the time of culture were not considered separately and were coded as removed only if all of the lines present at culture had been removed.

Severity of illness prior to the bacteremia of interest was assessed by calculating a modified acute physiology score (APS) based on the APACHE III score (19) at admission and again at 24 h before the time the culture was obtained. We chose this latter time point in order to make sure that the aggregate score accurately reflects the baseline severity of illness for each patient and did not include values that occurred as a consequence of the bacteremia. The APACHE III score was designed for use among intensive care unit (ICU) patients. Since the present study included participants who may not have been in an ICU at the time of the bacteremia, we modified the score by excluding variables that were irrelevant to our study population such as pulmonary arterial gradient, urine output, neurological status, and ventilator data.

The presence of preexisting comorbid conditions was determined using the chronic disease score (CDS), which utilizes patient medications as indicators of the existence of comorbid conditions (32). In the present study, the CDS was calculated based upon the patient medications ordered within the first 24 h of hospital admission, as has been done in other studies (17, 24).

Statistical analysis.

All data were collected and entered into a Microsoft Access database (Microsoft Corp., Redmond, WA) and analyzed by using SAS (SAS Institute, Cary, NC) software version 9.1. The Fisher exact test and chi-square test were used to compare categorical variables, and the Student t test was used for continuous variables. The Wilcoxon rank-sum test was used to test the difference between the median values for non-normally distributed continuous variables.

Multivariable regression was used to control for confounding variables. Since the APS at the time of culture collection and the APS at the time of admission are inherently correlated variables, they were entered into the regression models separately during analysis. All biologically plausible variables with a P value of <0.20 in the bivariable analysis were considered for inclusion in the final multivariable regression models. As our primary variable of interest, appropriate empirical antibiotic therapy was forced into all multivariable models. Multivariable analysis was performed using backward selection logistic regression for the outcome of death. Linear regression was used for the outcome of length of stay (LOS). Because of the non-normal distribution of LOS, this variable was natural log transformed in the linear regression analyses. In this form, the exponentiated model coefficients represent the ratio of the natural log of LOS between levels of the independent variables. Patients who died in the hospital were excluded from the LOS analyses because their hospital stay was shortened by death. All tests of significance were two-tailed, and P values of ≤0.05 were considered significant.


During the study period, 179 episodes of P. aeruginosa bacteremia were identified for inclusion. Twelve patients did not have a complete medication administration record in their charts and were thus excluded from the analysis. The mortality for these excluded patients was 33.3%, and the median LOS was 15.3 days (compared to 36.5% and 10.5 days in the included population). Of the 167 remaining, 159 episodes of bacteremia were unique individuals, six patients had at least two qualifying episodes and two of these six patients were included three times. The index blood culture was obtained within 72 h of admission for 61 (36.5%) of the patients in the cohort. A total of 50 (29.9%) episodes were polymicrobial bacteremia. The mean age of the cohort was 55 ± 16.2 years (range, from 20 to 89 years), and 111 subjects (66.5%) were male. Only 11 (6.5%) patients were in an ICU when the culture of interest was obtained. Table Table11 outlines the characteristics of survivors and nonsurvivors included in the study.

Characteristics of hospital survivors compared to nonsurvivors

A total of 99 (59.3%) patients received appropriate antibiotics within the first 24 h after the time at which the index blood culture was drawn; 114 (72.6%) received appropriate therapy within 48 h, and 123 (86%) patients received appropriate antibiotics by the time the susceptibility results became available. Twenty-four (16.7%) patients had not yet received appropriate antibiotics at the time the susceptibility results were available, and twenty patients (11.9%) were either discharged or had died by this time point. The mean time between culture collection and receipt of the antibiotic susceptibility testing results was 3.4 days (range, 1.0 to 10.4 days).

Of the isolates recovered during the study period, 82.6% were susceptible to piperacillin-tazobactam, 83.2% were susceptible to imipenem, and 84.5% were susceptible to cefepime. Twenty-seven isolates were tested for susceptibility to polymixin B, and none of these were resistant (Table (Table22).

Antibiotic susceptibility profiles

The mean modified APS score at admission (admission APS) was 25.3 ± 12.5(range, 1 to 73). The mean modified APS 24 h before culture collection (culture APS) was 29 ± 16.3 (range, 2 to 74). For 56 episodes of bacteremia, the index blood culture was drawn within 24 h of hospital admission and therefore had similar culture APS and admission APS scores.

Hospital mortality.

Sixty-one (36.5%) patients died during their hospital stay. In bivariable analysis, there was a trend toward a protective effect of receiving appropriate antibiotics between 8 h before and 24 h after the index blood culture collection (odds ratio [OR] = 0.88; 95% confidence interval [CI] = 0.38 to 1.4), between 24 and 48 h (OR = 0.68; 95% CI = 0.33 to 1.4), and at the time susceptibility results were available (OR = 0.71; 95% CI = 0.26 to 1.93). Compared to survivors, hospital nonsurvivors were significantly more likely to be older (OR = 1.02; 95% CI = 1.00 to 1.05), ventilated at the time of culture (OR = 2.14; 95% CI = 1.09 to 4.20), and have a higher modified APS at admission (OR = 1.04; 95% CI = 1.01 to 1.07) and at the time of culture (OR = 1.06; 95% CI = 1.03 to 1.09). In addition, time prior to blood culture collection was significantly longer in patients who died compared to those who survived (11.05 days compared to 4.3 days; P = 0.01).

In the multivariable logistic regression analysis (Table (Table3),3), after we adjusted for the APS at time of culture and age, there was a trend toward a protective effect of appropriate antibiotics at 24 h (OR = 0.93; 95% CI = 0.45 to 1.92), at 48 h (OR = 0.66; 95% CI = 0.29 to 1.49), and at the time susceptibility results were available (OR = 0.96; 95% CI = 0.31 to 2.93). The variables age and APS were modeled continuously, such that the odds ratios are representative of a 1-year or one-point increase, respectively.

Multivariable analysis

Length of stay.

The median length of stay in the hospital after collection of the index blood culture was 10.5 days. Variables statistically associated with an increased length of stay in bivariable analysis were time at risk (P < 0.01) and the presence of a ventilator at admission (P = 0.02) and at the time the culture was obtained (P < 0.01). Also, compared to patients who never had a central line, patients who had a central line at culture (P = 0.04), patients who had a central line that was removed 24 h (P = 0.03) and or removed at 48 h (P = 0.03) after the culture was obtained, and patients who had a line that was never removed (P < 0.01) had significantly longer LOS values.

In multivariable linear regression analysis, there was no association between receiving appropriate therapy between 8 h before and 24 h after the time of culture and an increase in LOS (ratio of LOS = 1.08; P = 0.66). Patients who received appropriate antibiotics at subsequent windows, i.e., between 24 and 48 h after the culture was obtained (ratio of LOS = 0.92; P = 0.62) and from 48 h until the time the susceptibility results were available (ratio of LOS = 0.93; P = 0.74), also did not have a significant change in their length of stay (Table (Table33).

Significant predictors of longer LOS in the multivariable analysis were time before blood culture collection (P < 0.01), the presence of a ventilator at admission (P = 0.03), and the presence of a ventilator at culture (P < 0.01).

Forty-eight (28.7%) patients had a positive blood culture within 12 h of their hospital stay. These patients had a lower mortality than patients whose index blood culture was collected later in their hospital stay. For these patients, in a multivariable model, APS at admission was a predictor of mortality (OR = 1.06. 95% CI = 1.03 to 1.09), and receiving appropriate therapy in the first 24 h was not statistically significantly associated with mortality (OR = 0.79, 95% CI = 0.43 to 1.44).


Our data show that there is a trend toward increased mortality in the group of patients who received inappropriate empirical treatment for P. aeruginosa bacteremia, but this was not statistically significant. In both bivariable and multivariable analysis, inappropriate empirical therapy was not found to be an independent predictor for hospital mortality. Our secondary outcome, length of stay, showed the same trend toward a longer length of stay for patients receiving inappropriate therapy, but this association also failed to reach statistical significance after controlling for potential confounders.

As expected, the independent predictors of mortality in our cohort were age and APS at culture. Increased time prior to blood culture collection (time from hospital admission to when the index blood culture was obtained) was associated with an increased length of stay. The presence of a central line at the time of culture was also an independent predictor of an increased length of stay.

The belief that early appropriate therapy should improve survival is intuitive but has not been clearly established in the literature. In fact, several studies of the influence of appropriate therapy in bloodstream infections have failed to find an association between adequate therapy and mortality. Zaragoza et al. found no difference in mortality in 166 ICU patients with bacteremia (34). Likewise, Bryan et al. (2) reported that appropriate therapy on the first calendar day after culture did not improve survival in an analysis of 1,186 episodes of gram-negative bacteremia. In contrast, these authors found that appropriate therapy, when started on the second calendar day after culture, had a protective effect compared to patients who received appropriate therapy from the onset of bacteremia. These researchers attributed these findings to patient factors, hypothesizing that patients who had survived the initial period of bacteremia without treatment were also more likely to survive their hospital stay. Several other large studies of gram-negative bacteremia have shown a statistically significant association between appropriate antibiotics and mortality (5, 13, 15, 20-23, 30). However, these studies included all gram-negative organisms and thus may have been underpowered to show a difference in survival for those patients who specifically had P. aeruginosa bacteremia.

To our knowledge, despite several publications on patient outcomes in P aeruginosa bacteremia (1, 3, 4, 16, 31), only two other studies were specifically designed to evaluate the impact of appropriate empirical antibiotics on the outcomes of P. aeruginosa bacteremia. Micek et al. reported on 305 patients with P. aeruginosa bacteremia and concluded that the association between appropriate initial therapy and hospital mortality was statistically significant but only after the variable for comorbidity (all patient refined diagnosis related group, APR-DRG) was removed from the multivariable model, which is not epidemiologically a suggested practice (25). On the other hand, Kang et al. established that the 30-day mortality in patients who experienced a delay in receiving appropriate antibiotics (after excluding those who did not get appropriate definitive therapy) tended to be higher, but this was not statistically significant, a finding similar to our results (14).

These inconsistent findings may be attributable to variations in the methodology used, differences in the study population, or differences in the distribution of other confounding variables among the studies. In reviewing the methodology, the studies differed in their definition of empirical therapy, the time at which severity of illness was measured, and whether septic shock was controlled for in the final regression models.

Antibiotics are generally defined as empirical if they are chosen before susceptibility results are known. In practice however, microbiology laboratories often report preliminary results of positive blood cultures (e.g., non-lactose-fermenting gram-negative rod), prompting treating physicians to change antibiotics based on an increased index of suspicion for one or the other organisms. Thus, the time point at which therapy is truly empirical is difficult to define. A cross-sectional look at the adequacy of therapy can misclassify patients who either were already receiving early definitive therapy or those who had received appropriate therapy earlier in their course. Micek et al. defined inappropriate antimicrobial treatment as “microbiological documentation of infection that was not adequately treated at the time the causative microorganism and its antibiotic susceptibility were known.” This definition therefore incorporates both empirical and definitive therapy. Kang et al. defined empirical therapy as effective if administered within 24 h after the blood culture was obtained or delayed if more than 24 h had elapsed. However, this definition did not take into consideration any antibiotics administered before the blood culture was collected, nor did it extend the assessment period to the time at which susceptibility results were available and thus did not analyze the full empirical therapy window.

Severity of illness may be an important confounder in patients who have bacteremia. A high severity of illness at the onset of the bacteremia may lead to a higher mortality but may also lead to more aggressive treatment early on. Ideally, when controlling for severity of illness as a predictor for mortality, it should be assessed before the onset of the bacteremia and would therefore reflect the patient's baseline risk of mortality. Measuring severity of illness exactly at the time the blood culture is drawn or any time after the onset of bacteremia would include variables that are a consequence of the current illness and therefore be in the causal pathway to mortality. These should not be controlled for since they would lead to an underestimate of the effect of inappropriate therapy on outcome (28). Likewise, respiratory failure and circulatory shock are usually the result of bacteremia and should not be adjusted for in the analysis (7, 29). Both of the previously published studies measured the severity of the illness at the time of bacteremia and adjusted for septic shock in their analyses.

In the present study, we calculated the severity of illness at a time point 24 h before the index blood culture was obtained whenever possible, therefore avoiding the inclusion of intermediate variables in our analysis. We also assessed appropriate therapy at three distinct time points, taking into account antibiotics administered before the blood culture was obtained and at various time points until the susceptibility results were available.

Our study was limited by a relatively low statistical power due to the fact that few patients in our cohort received inappropriate empirical therapy. This may be explained by the fact that most of the isolates of P. aeruginosa included in our study were susceptible to piperacillin-tazobactam, a beta-lactam antibiotic that is recommended for use in our institution in cases where a nosocomial or gram-negative infection is suspected. Although P. aeruginosa is rarely a contaminant of blood cultures (33), it is possible that some of the patients in this study were incorrectly classified as having a true bacteremia. We did not have data on the number of blood cultures positive with P. aeruginosa and thus were unable to try to sort out true P. aeruginosa pathogens from contaminants. Also, for patients who had polymicrobial bacteremia, we were unable to completely assess whether all coinfecting species were being appropriately treated along with the P. aeruginosa bacteremia. However, statistical analyses where patients with polymicrobial bacteremia were excluded did not yield any different results. Hospital mortality may also be influenced by inappropriate definitive therapy or early termination of appropriate therapy, neither of which we assessed in our study.

In conclusion, P. aeruginosa bacteremia is an important cause of hospital mortality but the effect of appropriate empirical therapy on survival is still unclear. We found that appropriate empirical therapy over time had a trend toward decreasing mortality and length of stay but neither was statistically significant. Thus far, none of the studies designed to assess the impact of appropriate empirical therapy on outcomes have been able to show a protective effect of appropriate empirical therapy on mortality without controlling for intermediate variables such as septic shock and severity of illness or not, including potentially important confounding variables in their final analyses. Therefore, further studies designed to evaluate the exact time point at which administration of an appropriate antibiotic is critical to survival are necessary. Identifying the time at which appropriate therapy becomes critical to patient outcomes is particularly important because of the delicate balance that exists between the beneficial effect of appropriate therapy on patient survival and the development of antimicrobial resistance due to excessive use of broad-spectrum antimicrobial agents.


This study was supported by National Institutes of Health grants 1R01A160859-01A1 and 1K23AI001752-01A1 (A.D.H.), U.S. Department of Veterans Affairs grants RCD-02-026-2 and IIR-05-123-1 (E.N.P.), and National Institutes of Health grant 1K12RR023250-01 (J.P.F.).

We thank Colleen Reilly and Jingkun Zhu for database maintenance and abstraction.


[down-pointing small open triangle]Published ahead of print on 28 December 2006.


1. Bodey, G. P., L. Jadeja, and L. Elting. 1985. Pseudomonas bacteremia: retrospective analysis of 410 episodes. Arch. Intern. Med. 145:1621-1629. [PubMed]
2. Bryan, C. S., K. L. Reynolds, and E. R. Brenner. 1983. Analysis of 1,186 episodes of gram-negative bacteremia in non-university hospitals: the effects of antimicrobial therapy. Rev. Infect. Dis. 5:629-638. [PubMed]
3. Chamot, E., E. Boffi El Amari, P. Rohner, and C. Van Delden. 2003. Effectiveness of combination antimicrobial therapy for Pseudomonas aeruginosa bacteremia. Antimicrob. Agents Chemother. 47:2756-2764. [PMC free article] [PubMed]
4. Chatzinikolaou, I., D. Abi-Said, G. P. Bodey, K. V. Rolston, J. J. Tarrand, and G. Samonis. 2000. Recent experience with Pseudomonas aeruginosa bacteremia in patients with cancer: retrospective analysis of 245 episodes. Arch. Intern. Med. 160:501-509. [PubMed]
5. Endimiani, A., A. Tamborini, F. Luzzaro, G. Lombardi, and A. Toniolo. 2003. A two-year analysis of risk factors and outcome in patients with bloodstream infection. Jpn. J. Infect. Dis. 56:1-7. [PubMed]
6. Furuno, J. P., A. D. Harris, M. O. Wright, J. C. McGregor, R. A. Venezia, J. Zhu, and E. N. Perencevich. 2004. Prediction rules to identify patients with methicillin-resistant Staphylococcus aureus and vancomycin-resistant enterococci upon hospital admission. Am. J. Infect. Control 32:436-440. [PubMed]
7. Greenland, S., J. Pearl, and J. M. Robins. 1999. Causal diagrams for epidemiologic research. Epidemiology 10:37-48. [PubMed]
8. Harris, A., C. Torres-Viera, L. Venkataraman, P. DeGirolami, M. Samore, and Y. Carmeli. 1999. Epidemiology and clinical outcomes of patients with multiresistant Pseudomonas aeruginosa. Clin. Infect. Dis. 28:1128-1133. [PubMed]
9. Harris, A. D., L. Nemoy, J. A. Johnson, A. Martin-Carnahan, D. L. Smith, H. Standiford, and E. N. Perencevich. 2004. Co-carriage rates of vancomycin-resistant Enterococcus and extended-spectrum beta-lactamase-producing bacteria among a cohort of intensive care unit patients: implications for an active surveillance program. Infect. Control Hosp. Epidemiol. 25:105-108. [PubMed]
10. Harris, A. D., E. Perencevich, M. C. Roghmann, G. Morris, K. S. Kaye, and J. A. Johnson. 2002. Risk factors for piperacillin-tazobactam-resistant Pseudomonas aeruginosa among hospitalized patients. Antimicrob. Agents Chemother. 46:854-858. [PMC free article] [PubMed]
11. Harris, A. D., D. Smith, J. A. Johnson, D. D. Bradham, and M. C. Roghmann. 2002. Risk factors for imipenem-resistant Pseudomonas aeruginosa among hospitalized patients. Clin. Infect. Dis. 34:340-345. [PubMed]
12. Hilf, M., V. L. Yu, J. Sharp, J. J. Zuravleff, J. A. Korvick, and R. R. Muder. 1989. Antibiotic therapy for Pseudomonas aeruginosa bacteremia: outcome correlations in a prospective study of 200 patients. Am. J. Med. 87:540-546. [PubMed]
13. Ibrahim, E. H., G. Sherman, S. Ward, V. J. Fraser, and M. H. Kollef. 2000. The influence of inadequate antimicrobial treatment of bloodstream infections on patient outcomes in the ICU setting. Chest 118:146-155. [PubMed]
14. Kang, C. I., S. H. Kim, H. B. Kim, S. W. Park, Y. J. Choe, M. D. Oh, E. C. Kim, and K. W. Choe. 2003. Pseudomonas aeruginosa bacteremia: risk factors for mortality and influence of delayed receipt of effective antimicrobial therapy on clinical outcome. Clin. Infect. Dis. 37:745-751. [PubMed]
15. Kang, C. I., S. H. Kim, W. B. Park, K. D. Lee, H. B. Kim, E. C. Kim, M. D. Oh, and K. W. Choe. 2005. Bloodstream infections caused by antibiotic-resistant gram-negative bacilli: risk factors for mortality and impact of inappropriate initial antimicrobial therapy on outcome. Antimicrob. Agents Chemother. 49:760-766. [PMC free article] [PubMed]
16. Kang, C. I., S. H. Kim, W. B. Park, K. D. Lee, H. B. Kim, E. C. Kim, M. D. Oh, and K. W. Choe. 2005. Risk factors for antimicrobial resistance and influence of resistance on mortality in patients with bloodstream infection caused by Pseudomonas aeruginosa. Microb. Drug Resist. 11:68-74. [PubMed]
17. Kaye, K. S., K. Sands, J. G. Donahue, K. A. Chan, P. Fishman, and R. Platt. 2001. Preoperative drug dispensing as predictor of surgical site infection. Emerg. Infect. Dis. 7:57-65. [PMC free article] [PubMed]
18. Kim, P. W., A. D. Harris, M. C. Roghmann, J. G. Morris, Jr., A. Strinivasan, and E. N. Perencevich. 2003. Epidemiological risk factors for isolation of ceftriaxone-resistant versus -susceptible Citrobacter freundii in hospitalized patients. Antimicrob. Agents Chemother. 47:2882-2887. [PMC free article] [PubMed]
19. Knaus, W. A., D. P. Wagner, E. A. Draper, J. E. Zimmerman, M. Bergner, P. G. Bastos, C. A. Sirio, D. J. Murphy, T. Lotring, A. Damiano, et al. 1991. The APACHE III prognostic system: risk prediction of hospital mortality for critically ill hospitalized adults. Chest 100:1619-1636. [PubMed]
20. Kollef, M. H., G. Sherman, S. Ward, and V. J. Fraser. 1999. Inadequate antimicrobial treatment of infections: a risk factor for hospital mortality among critically ill patients. Chest 115:462-474. [PubMed]
21. Lautenbach, E., N. O. Fishman, W. B. Bilker, A. Castiglioni, J. P. Metlay, P. H. Edelstein, and B. L. Strom. 2002. Risk factors for fluoroquinolone resistance in nosocomial Escherichia coli and Klebsiella pneumoniae infections. Arch. Intern. Med. 162:2469-2477. [PubMed]
22. Lee, S. C., C. C. Hua, T. J. Yu, W. B. Shieh, and L. C. See. 2005. Risk factors of mortality for nosocomial pneumonia: importance of initial anti-microbial therapy. Int. J. Clin. Pract. 59:39-45. [PubMed]
23. MacArthur, R. D., M. Miller, T. Albertson, E. Panacek, D. Johnson, L. Teoh, and W. Barchuk. 2004. Adequacy of early empiric antibiotic treatment and survival in severe sepsis: experience from the MONARCS trial. Clin. Infect. Dis. 38:284-288. [PubMed]
24. McGregor, J. C., P. W. Kim, E. N. Perencevich, D. D. Bradham, J. P. Furuno, K. S. Kaye, J. C. Fink, P. Langenberg, M. C. Roghmann, and A. D. Harris. 2005. Utility of the Chronic Disease Score and Charlson Comorbidity Index as comorbidity measures for use in epidemiologic studies of antibiotic-resistant organisms. Am. J. Epidemiol. 161:483-493. [PubMed]
25. Micek, S. T., A. E. Lloyd, D. J. Ritchie, R. M. Reichley, V. J. Fraser, and M. H. Kollef. 2005. Pseudomonas aeruginosa bloodstream infection: importance of appropriate initial antimicrobial treatment. Antimicrob. Agents Chemother. 49:1306-1311. [PMC free article] [PubMed]
26. National Committee for Clinical Laboratory Standards. 2000. Methods for dilution antimicrobial susceptibility tests for bacteria that grow aerobically, 7th ed. Approved standard M7-A7. National Committee for Clinical Laboratory Standards, Wayne, Pa.
27. Pefanis, A., H. Giamarellou, P. Karayiannakos, and I. Donta. 1993. Efficacy of ceftazidime and aztreonam alone or in combination with amikacin in experimental left-sided Pseudomonas aeruginosa endocarditis. Antimicrob. Agents Chemother. 37:308-313. [PMC free article] [PubMed]
28. Perencevich, E. N. 2000. Excess shock and mortality in Staphylococcus aureus related to methicillin resistance. Clin. Infect. Dis. 31:1311-1313. [PubMed]
29. Rothman, K. J., and S. Greenland. 2005. Causation and causal inference in epidemiology. Am. J. Public Health 95(Suppl. 1):S144-S150. [PubMed]
30. Spanik, S., D. West, P. Pichna, J. Novotny, J. Dacok, M. Mraz, B. Chmelik, I. Krupova, and V. Krcmery, Jr. 1998. Inappropriate antibiotic therapy in febrile cancer patients with bacteremia. Clin. Perform. Qual. Health Care 6:109-113. [PubMed]
31. Vidal, F., J. Mensa, M. Almela, J. A. Martinez, F. Marco, C. Casals, J. M. Gatell, E. Soriano, and M. T. Jimenez de Anta. 1996. Epidemiology and outcome of Pseudomonas aeruginosa bacteremia, with special emphasis on the influence of antibiotic treatment. Analysis of 189 episodes. Arch. Intern. Med. 156:2121-2126. [PubMed]
32. Von Korff, M., E. H. Wagner, and K. Saunders. 1992. A chronic disease score from automated pharmacy data. J. Clin. Epidemiol. 45:197-203. [PubMed]
33. Weinstein, M. P., M. L. Towns, S. M. Quartey, S. Mirrett, L. G. Reimer, G. Parmigiani, and L. B. Reller. 1997. The clinical significance of positive blood cultures in the 1990s: a prospective comprehensive evaluation of the microbiology, epidemiology, and outcome of bacteremia and fungemia in adults. Clin. Infect. Dis. 24:584-602. [PubMed]
34. Zaragoza, R., A. Artero, J. J. Camarena, S. Sancho, R. Gonzalez, and J. M. Nogueira. 2003. The influence of inadequate empirical antimicrobial treatment on patients with bloodstream infections in an intensive care unit. Clin. Microbiol. Infect. 9:412-418. [PubMed]

Articles from Antimicrobial Agents and Chemotherapy are provided here courtesy of American Society for Microbiology (ASM)
PubReader format: click here to try


Related citations in PubMed

See reviews...See all...

Cited by other articles in PMC

See all...


Recent Activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...