clin infect dis. 2014 lodise 666 75

M A J O R A R T I C L E

Vancomycin Exposure in Patients WithMethicillin-Resistant Staphylococcus aureusBloodstream Infections: How Much Is Enough?

Thomas P. Lodise,1 George L. Drusano,2 Evan Zasowski,1 Amanda Dihmess,1 Victoria Lazariu,3 Leon Cosler,1 andLouise-Anne McNutt3

1Albany College of Pharmacy and Health Sciences, New York; 2Institute for Therapeutic Innovation, College of Medicine, University of Florida, Lake Nona;and 3University at Albany, State University of New York

Background. Contemporary vancomycin dosing schemes are designed to achieve an area under the curve(AUC) to minimum inhibitory concentration (MIC) ratio of ≥400. However, scant clinical data exist to supportthis target and available data relied on pharmacokinetic formulas based on daily vancomycin dose and estimatedrenal function (demographic pharmacokinetic model) to estimate AUCs.

Methods. A cohort study of hospitalized, adult, nondialysis patients with methicillin-resistant Staphylococcusaureus bloodstream infections treated with vancomycin was performed to quantitatively evaluate the relationshipbetween vancomycin exposure and outcomes. Bayesian techniques were used to estimate vancomycin exposure pro-file for day 1 and 2 of therapy for each patient based on their dosing schedule and collected concentrations. Clas-sification and Regression Tree (CART) analysis was used to identify day 1 and 2 exposure thresholds associated withan increased risk of failure. Failure was defined as 30-day mortality, bacteremia was ≥7 days, or recurrence.

Results. During the study period, 123 cases met criteria. Failure was uniformly less pronounced (approximately20% less in absolute value) in patients who achieved the CART-derived day 1 and 2 thresholds for AUC/MIC bybroth microdilution and AUC/MIC by Etest. In the multivariate analyses, all risk ratios were approximately 0.5 for allCART-derived AUC/MIC exposure thresholds, indicating that achievement of CART-derived AUC/MIC exposurethresholds was associated with a 2-fold decrease in failure.

Conclusions. These findings establish the critical importance of daily AUC/MIC ratios during the first 2 days oftherapy. As with all observational studies, these findings should be interpreted cautiously and validated in a multi-center randomized trial before adoption into practice.

Keywords. MRSA; outcomes; pharmacodynamics; pharmacokinetics; vancomycin.

Despite its introduction more than a half-century ago,the optimal dosing strategy for vancomycin remainsundefined. Contemporary vancomycin dosing schemesare designed to achieve an area under the curve (AUC)to minimum inhibitory concentration (MIC) ratio≥400 for serious methicillin-resistant Staphylococcus

aureus (MRSA) infections [1, 2]. Although this targetis based on the best available evidence [1–6], it is largelyderived from neutropenic mouse thigh infection modeldata [3]. The best clinical evidence supporting AUC/MIC ratio ≥400 is drawn from a retrospective evalua-tionof patientswith S. aureuspneumonia [5].Two recentstudies of patients with MRSA bloodstream infections(BSIs) have also identified similar vancomycin AUC/MIC ratio targets [7, 8]. Although these evaluationsprovide further evidence that the vancomycin pharma-codynamic target is an AUC/MIC ratio of at least 400,all evaluations used a simple formula based on daily van-comycin dose and estimated renal function to estimateAUC values [5, 7, 8]. In most cases, they used the Cock-croft-Gault creatinine clearance (CrCl) formula. There

Received 4 March 2014; accepted 19 May 2014; electronically published 27 May2014.

Correspondence: Thomas Lodise, PharmD, PhD, Pharmacy Practice, Albany Col-lege of Pharmacy and Health Sciences, Albany, NY 12208-3492 ([email protected]).

Clinical Infectious Diseases 2014;59(5):666–75© The Author 2014. Published by Oxford University Press on behalf of the InfectiousDiseases Society of America. All rights reserved. For Permissions, please e-mail:[email protected]: 10.1093/cid/ciu398

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is considerable interpatient variability in vancomycin exposureprofiles in clinical practice and it is difficult to generate valid es-timates of exposure variables in a given individual based on glo-merular filtration estimation formulas alone [9–11]. To date, weare only aware of 2 small-scale vancomycin exposure–responseclinical evaluations that considered individualized estimates ofthe vancomycin AUC based on collected levels and doses received[12, 13]. Thus, there is a critical need for additional, larger-scaleclinical studies that utilize individualized estimates of exposureprofiles based on measured concentrations.

Although AUC/MIC ratio is the prevailing vancomycin expo-sure target, AUCs are not determined in clinical practice due tothe perceived difficulty in calculating the AUC [2]. Expert guide-lines recommend maintaining a minimum (“trough”) concentra-tion (Cmin) between 15 and 20 mg/L as a surrogate marker for anAUC/MIC ratio ≥400 [1, 2]. However, the clinical benefits ofmaintaining higher vancomycin trough values have not beenwell described [14–19]. The intent of this study was to quantita-tively evaluate the relationship between vancomycin exposurevariables (ie, Cmin/MIC, AUC/MIC) and outcomes among pa-tients with MRSA BSIs. Bayesian techniques [20–22] were usedto estimate the vancomycin concentration–time profile for eachpatient. The Bayesian approach used in this study to estimateexposure profiles has recently been validated as a method to esti-mate vancomycin exposure values with low bias and high preci-sion in situations where trough-only pharmacokinetic (PK) dataare available [22]. As a secondary objective, this study comparedthe predictive performance of the Bayesian relative to the formula-based approach for estimating exposure profiles.

METHODS

Experimental Design and Study PopulationA retrospective cohort study was performed among hospitalizedpatients with MRSA BSIs treated with vancomycin at AlbanyMedical Center Hospital between January 2005 and June2009. Patients meeting the following criteria were included:(1) age ≥18 years; (2) absolute neutrophil count ≥1000 cells/µL; (3) MRSA culture met the Centers for Disease Controland Prevention criteria for BSI [23]; (4) index MRSA isolateavailable for phenotypic characterization; (5) not receiving dial-ysis; (6) received vancomycin within 48 hours of index culture;(7) received vancomycin for at least 2 days; and (8) had ≥1 van-comycin level collected within the first 5 days of therapy. If apatient had >1 MRSA BSI during the study period, additionalepisodes were included if they occurred >60 days after comple-tion of antibiotic therapy for the previous BSI. The study waslimited to patients who received vancomycin within 48 hoursof index culture collection as this has been identified as thecritical time window for delivery of appropriate antibioticsfor patients with MRSA BSIs [24]. The study was approved

by expedited review by the institutional review board ofAlbany Medical Center Hospital, and a HIPAA waiver wasobtained.

Patient DataData elements included demographics, medical history, and co-morbidities [18], recent healthcare institution exposure in thepast 6 months, receipt of antibiotics in the 30 days prior tothe index culture collection, hospitalization history, and CrClestimated by the Cockcroft-Gault formula [25] at index culturecollection. Illness severity was defined by the Acute Physiologyand Chronic Health Evaluation II score (based on the worstphysiological score in the 48 hours prior to index culture collec-tion) [26] and the Chronic Disease Score–Infectious Diseasesscore (determined at admission) [27]. Additional data elementsincluded source of MRSA BSI, mortality risk associated with in-fection source [24, 28, 29], presence of infective endocarditis[30], infection source control intervention, microbiologic data,treatment data, occurrence of nephrotoxicity (defined as either a50% or 0.5 mg/dL increase in serum creatinine, whichever wasgreater, from initiation of vancomycin to 48 hours postcomple-tion among patients with a baseline serum creatinine level<2 mg/dL [2]), and outcomes.

Microbiologic Data and Phenotypic and GenotypicCharacterizationAll isolates were identified as S. aureus according to standardmethods. Initial susceptibility testing for oxacillin resistancewas performed according to Clinical and Laboratory StandardsInstitute guidelines [11]. Isolates were stored in trypticase soybroth with 20% glycerol at −70°C. All available MRSA BSIswere shipped to JMI Laboratories (North Liberty, Iowa) for de-termination of broth microdilution (BMD) MIC [31], EtestMIC (according to the manufacturer’s instructions; bioMérieux,Marcy l’Etoile, France), minimum bactericidal concentration(MBC) [32], heterogeneous vancomycin-intermediate S. aureus(hVISA) [33], and δ-lysin activity [34]. Details on these testingmethodologies are provided in the Supplementary Methods.For patients with multiple MRSA blood cultures, only theindex isolate was considered in the analysis.

Treatment DataAll antibiotic treatment and vancomycin concentration datawere collected. Vancomycin exposure variables were estimatedusing ADAPT 5, a “computational modeling platform devel-oped for PK/PD [pharmacodynamic] applications” [20].Given the time-critical nature of the first 48 hours of treatmentfor MRSA BSIs [24], vancomycin exposure variables were esti-mated using the maximal a posteriori probability (MAP) proce-dure in ADAPT 5 for hours 0–24 (day 1) and 24–48 (day 2) [20,21]. This approach has recently been validated as a way to

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estimate AUC values with low bias and high precision withtrough-only PK sampling [22].

In brief, the mean parameter vector and covariance matrixfrom a previously described 2-compartment vancomycinmodel [35–37] was embedded in the PRIOR subroutine ofADAPT 5 (Bayesian prior) [20]. The MAP procedure in estima-tion module (ID) of ADAPT 5 was then used to estimate the re-vised probability distribution of a given patient’s PK parametervalues after dosing and drug concentration data were takeninto account (Bayesian conditional posterior). With the Bayesianposterior PK information for a given individual, ADAPT 5 wasused to estimate the following vancomycin exposure variablesbased on the dosing schedule received: (1) Cmin24h, (2) Cmin48h,(3) AUC0−24h, and (4) AUC24−48h. The predictive performanceof the Bayesian approach was assessed by comparing the predict-ed to the observed concentrations. The predictive performance ofthe Cockcroft-Gault formula–based approach using the mean pa-rameter vector of the PK model [35–37] used in this study wasalso determined.

OutcomesDue to the retrospective nature of the study, an objective assess-ment of treatment failure that included only readily measurablestudy endpoints was used [18, 38]. Failure was defined as any ofthe following: (1) death within 30 days of index MRSA bloodculture (30-day mortality), (2) microbiological failure (bloodculture growing MRSA obtained 7 days after the initiation oftherapy and before therapy completion), or (3) recurrence ofMRSA bacteremia within 60 days of discontinuation of therapy[18, 38]. The Social Security Death Index was accessed to deter-mine the vital status of patients discharged within 30 days ofMRSA bacteremia onset.

Data Analysis PlanThe primary vancomycin exposure variables considered in theanalyses included: (1) Cmin24h/MICBMD, (2) Cmin48h/MICBMD,(3) AUC0−24h/MICBMD, (4) AUC24−48h/MICBMD, (5) Cmin24h/MICETEST, (6) Cmin48h/MICETEST, (7) AUC0−24h/MICETEST,and (8) AUC24−48h/MICETEST. Vancomycin exposure variableswere modeled as both continuous and dichotomous variables.Bivariate associations between vancomycin exposure variablesand outcomes and baseline covariates and outcomes were as-sessed using Fisher exact test (categorical variables) and Studentt test and Mann–Whitney U test (continuous variables). Break-points in the distribution of continuous vancomycin exposurevariables were sought by Classification and Regression Tree(CART) analysis [39]. We also examined the relationship be-tween Cmin ≥15 mg/L and outcomes, given the recent expertguidelines recommendations [1, 2].

Poisson regression analyses with robust variance estimates[40, 41] were performed to quantify the association between

each vancomycin exposure variable and failure and 30-daymortality after adjustment for potential confounding variables.Each vancomycin exposure variable was evaluated separately.All potential confounding variables (baseline covariates asso-ciated with outcomes at P < .2) were included at model entryand were retained as confounders if the relative risk (RR) ofthe vancomycin exposure variable changed by >10%. All calcu-lations were computed using SAS software, version 9.3 (SAS In-stitute, Cary, North Carolina) and CART software (SalfordSystems, San Diego, California).

Table 1. Distribution of Microbiologic Phenotypes, ExposureVariables, and Outcomes in Study Cohort

Characteristic Value

Microbiologic phenotypes

MICETEST valuesRange 0.38–3.0 mg/L

MIC50/90 1.5/1.5 mg/L

MICBMD

Range 0.38–3.0 mg/L

MIC50/90 0.75/1 mg/L

MBC/MIC ratioRange 1–21.33

MBC/MIC50/90 1.3/2.4

hVISA phenotype 4 (3.3%)Agr dysfunctional 62 (50.4%)

Mean (SD) vancomycin exposure variables

Cmin24h 8.6 (4.7)Cmin48h 11.2 (5.9)

Cmin24h/MICBMD 11.2 (6.6)

Cmin48h/MICBMD 14.8 (8.5)Cmin24h/MICETEST 7.4 (5.3)

Cmin48h/MICETEST 9.7 (6.8)

AUC0−24h 436.4 (162.5)AUC24−48h 517.3 (197.3)

AUC0−24h/MICBMD 571.6 (245.8)

AUC24−48h/MICBMD 680.0 (302.2)AUC0−24h/MICETEST 380.1 (215.4)

AUC24−48h/MICETEST 453.9 (273.6)

OutcomesFailurea 40 (32.5%)

30-d mortality 25 (20.3%)

Microbiologic failure 15 (12.2%)Recurrence 10 (8.1%)

Nephrotoxicityb 19 (17.9%)

Abbreviations: AUC, area under the curve; BMD, broth microdilution; Cmin,minimum concentration; hVISA, heterogeneous vancomycin-intermediateS. aureus; MBC, minimum bactericidal concentration; MIC, minimuminhibitory concentration; MIC50/90, minimum concentration that inhibits 50%and 90% of bacterial isolates; SD, standard deviation.a Of the 40 failures, 30 met 1 failure criterion and 10 met 2 criteria.b Nephrotoxicity occurrences among the 106 patients with a serum creatininelevel <2 mg/dL.


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RESULTS

There were 238 unique episodes of MRSA BSIs during the ob-servation period, and 123 met the inclusion criteria. Reasons forexclusion were (1) no MRSA isolate (n = 18); (2) receipt of dial-ysis (n = 58); (3) treatment with alternative anti-MRSA antibi-otic (n = 20); (4) lack of anti-MRSA antibiotics (n = 10); and(5) no vancomycin measurements (n = 10). Among cases treat-ed with alternative antibiotics, most received either linezolid(n = 11) or daptomycin (n = 5). Distribution of microbiologicphenotypes, exposure variables, and outcomes among the 123included cases are provided in Table 1. In situations wheresource control was warranted, there was an attempt at sourcecontrol in most cases. Among patients who experienced “fail-ure,” there was a documented intervention in the medicalrecord to “control the source” in >95% of cases.

Results of the observed vs predicted plots for the Bayesianand formula-based estimation approaches are shown in Figure 1.There were 282 available concentrations among the 123 cases.The regression line from the observed–predicted plot for the

Bayesian approach was 0.994 × predicted + 0.08, and the R2

was 0.99 (Figure 1A). The regression line for the formula-based approach was 0.54 × predicted + 8.2 and the R2 was0.32 (Figure 1B).

Bivariate comparisons between vancomycin exposure vari-ables and failure are displayed in Figures 2A and 2B. Break-points were identified for all continuous vancomycin exposurevariables with CART. Cases with Cmin/MICBMD values in excessof the CART-derived thresholds had higher incidences of fail-ure, whereas the inverse was observed for the CART-derivedCmin/MICETEST variables (Figure 2A). Similarly, failure washigher among patients with trough levels ≥15 mg/L relative tothose with trough levels <15 mg/L on both day 1 (30.6% vs50.0%, respectively; P = .2) and day 2 (30.1% vs 40.0%, respec-tively; P = .3). In contrast, failure was uniformly less pronouncedin patients who achieved the AUC/MICBMD and AUC/MICETEST

CART-derived thresholds relative to those who did not (Fig-ure 2B). For all 4 AUC/MIC exposure variables, failure was nearlydoubled in patients with exposures below the CART-derivedthresholds vs those in excess of the breakpoints. Bivariate com-parisons between the CART-derived vancomycin exposure vari-ables and 30-day mortality, microbiologic failure, and recurrenceare shown in Table 2. Overall, the findings between the CART-derived vancomycin exposure variables and 30-day mortality, mi-crobiologic failure, and recurrence were generally consistent withthe exposure–failure analyses.

Results of the Poisson regression analyses for failure and30-day mortality are displayed in Table 3. Baseline covariatesassociated with failure or 30-day mortality at a P value <.2were considered as potential confounders at model entry ineach set of multivariate analyses (Table 4). Overall, no notablerelationships were observed between the CART-derived Cmin/MIC exposure variables and failure. In contrast, all adjusted rel-ative risks were <1 for the CART-derived AUC/MIC exposurevariables. Similar to the bivariate analyses, the risk of failurewas 50% lower in those with exposures in excess of theCART-derived AUC/MIC thresholds relative to those belowthe AUC/MIC ratio breakpoints. The results from the “30-daymortality” Poisson regression analyses were largely similar tothe “failure” Poisson regression analyses. All CART-derivedAUC/MIC exposures and Cmin0−24h/MICETEST ≥4.4 were asso-ciated with a lower risk of 30-day mortality.

DISCUSSION

Using a validated method to determine exposure profiles amongpatients with “trough-only” PK sampling [22], the analyses pre-sented here suggest that AUC/MIC is the pharmacodynamicindex most closely linked to outcomes for vancomycin.Achievement of exposure values in excess of the CART-derivedAUC/MIC breakpoints were associated with failure rates of

Figure 1. Observed vs predicted concentrations for Bayesian estimationapproach (A) and formula-based approach (B).


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20%–25%, consistent with the near-maximal effect expected forpatients with MRSA BSIs [18, 24, 42]. In contrast, failure toachieve the CART-derived AUC/MIC exposure thresholdswas associated with failure rates >40%. In the multivariate anal-yses (Table 3), all RRs were approximately 0.5, indicating thatachievement of CART-derived AUC/MIC exposure thresholds

was associated with a 2-fold decrease in failure. Similarly,achievement of the CART-derived AUC/MIC exposures was as-sociated with a 2–2.5 fold reduction in 30-day mortality. Collec-tively, these findings establish the critical importance of thedaily AUC/MIC ratios during the first 2 days of vancomycintherapy.

Figure 2. Bivariate relationship between Classification and Regression Tree (CART) analysis–derived day 1 and day 2 minimum concentration/minimuminhibitory concentration (MIC) exposure variables and failure (A) and CART-derived day 1 and day 2 area under the curve/MIC exposure variables and failure(B). Abbreviations: AUC, area under the curve; BMD, broth microdilution; CART, Classification and Regression Tree; CI, confidence interval; MIC, minimuminhibitory concentration; RR, relative risk.


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Unfortunately, this study lacked power to discriminate be-tween AUC/MICBMD and AUC/MICETEST and day 1 and 2 ex-posures. All CART-derived AUC/MIC exposure variables

across both time intervals (day 1 and day 2) were found to besimilarly associated with outcomes. Not surprisingly, theCART-derived exposure AUC/MICBMD and AUC/MICETEST

Table 2. Bivariate Comparisons Between Classification and Regression Tree Analysis–Derived Vancomycin Exposure Variables and30-Day Mortality, Microbiologic Failure, and Recurrence

Variable 30-d Mortality RR (95% CI) Microbiologic Failure RR (95% CI) Recurrence RR (95% CI)

Cmin/MIC (BMD)

Cmin0−24h/MIC≥ 14.9a (n = 28) 11 (39.3) 2.67 (1.37–5.20) 2 (7.1) 0.52 (.13–2.18) 1 (3.4) 0.38 (.05–2.85)Cmin0−24h/MIC < 14.9a (n = 95) 14 (14.7) 13 (13.7) 9 (9.5)

Cmin24−48h/MIC≥ 20.4a (n = 30) 10 (33.3) 2.07 (1.04–4.10) 5 (16.7) 1.55 (.58–4.18) 2 (6.7) 0.78 (.17–3.45)

Cmin24−48h/MIC < 20.4a (n = 93) 15 (16.1) 10 (10.8) 8 (8.6)Cmin/MIC (ETEST)

Cmin0−24h/MIC≥ 4.4a (n = 91) 15 (16.5) 0.53 (.26–1.05) 11 (12.1) 0.97 (.33–2.82) 8 (9.8) 1.41 (.32–6.28)

Cmin0−24h/MIC < 4.4a (n = 32) 10 (31.3) 4 (12.5) 2 (6.3)Cmin24−48h/MIC≥ 11.2a (n = 41) 8 (19.5) 0.94 (.44–2.00) 3 (7.3) 0.50 (.15–1.67) 2 (4.9) 0.50 (.11–2.25)

Cmin24−48h/MIC < 11.2a (n = 82) 17 (20.7) 12 (14.6) 8 (9.8)

AUC/MIC (BMD)AUC0−24h/MIC≥ 521a (n = 67) 11 (16.4) 0.66 (.32–1.33) 4 (6.0) 0.30 (.10–.90) 5 (7.5) 0.84 (.25–2.74)

AUC0−24h/MIC < 521a (n = 56) 14 (25) 11 (19.6) 5 (8.9)

AUC24−48h/MIC≥ 650a (n = 65) 10 (15.4) 0.59 (.29–1.22) 5 (7.7) 0.45 (.16–1.23) 5 (7.7) 0.89 (.27–2.93)AUC24−48h/MIC < 650a (n = 58) 15 (25.9) 10 (17.2) 5 (8.6)

AUC/MIC (ETEST)

AUC0−24h/MIC≥ 303a (n = 73) 9 (12.3) 0.39 (.19–.80) 7 (9.6) 0.60 (.23–1.54) 5 (6.8) 0.68 (.21–2.24)AUC0−24h/MIC < 303a (n = 50) 16 (32) 8 (16.0) 5 (10)

AUC24−48h/MIC≥ 320a (n = 85) 12 (14.1) 0.41 (.21–.82) 9 (10.6) 0.67 (.26–1.75) 7 (8.2) 1.04 (.29–3.82)

AUC24−48h/MIC < 320a (n = 38) 13 (38.2) 6 (15.8) 3 (7.9)

All data presented as No. (%) unless otherwise noted.

Abbreviations: AUC, area under the curve; BMD, broth microdilution; CI, confidence interval; Cmin, minimum concentration; MIC, minimum inhibitory concentration;RR, relative risk.a Classification and Regression Tree Analysis–derived breakpoints.

Table 3. Association Between the Classification and Regression Tree Analysis–Derived Vancomycin Minimum Concentration and AreaUnder the Curve Exposure Variables and Overall Failure and 30-Day Mortality in the Poisson Regression Analyses

Exposure

Overall Failurea 30-d Mortalityb

RR 95% CI P Value RR 95% CI P Value

Day 1 Cmin0−24 h/MICBMD≥ 14.9 1.24 .67–2.29 .50 1.65 .77–3.56 .19

Cmin0−24 h/MICETEST≥ 4.4 0.63 .37–1.08 .09 0.43 .22–.87 .02

AUC0−24 h/MICBMD≥ 521 0.54 .32–.91 .02 0.43 .20–.90 .03AUC0−24 h/MICETEST≥ 303 0.48 .29–.78 .003 0.32 .16–.64 .001

Day 2 Cmin24−48 h/MICBMD≥ 20.4 1.47 .79–2.54 .24 1.38 .69–2.75 .36

Cmin24−48 h/MICETEST≥ 11.2 0.80 .44–1.44 .46 0.97 .46–2.03 .93AUC24−48 h/MICBMD≥ 650 0.58 .34–.99 .05 0.50 .25–1.02 .06

AUC24−48 h/MICETEST > 320 0.53 .32–.88 .01 0.49 .24–.98 .04

Abbreviations: AUC, area under the curve; BMD, broth microdilution; CI, confidence interval; Cmin, minimum concentration; MBC, minimum bactericidalconcentration; MIC, minimum inhibitory concentration; RR, relative risk.a All variables associated with failure at P≤ .2 and considered at model entry included: Acute Physiology and Chronic Health Evaluation II (APACHE-II) score, chronicobstructive pulmonary disease, diabetes mellitus, malignancy, recent prior surgery, MICETEST ≥1.5 mg/L, and cumulative number of reduced vancomycinsusceptibility phenotypes.b Baseline covariates associated with 30-day mortality at P≤ .2 and considered at model entry included APACHE-II score, malignancy, MICETEST ≥1.5 mg/L, MICBMD

≥1 mg/L, and MBC/MIC ratio >4.


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Table 4. Bivariate Comparisons of Baseline Features Between Failures and 30-Day Mortality

Variable Failure (n = 40) Nonfailure (n = 83) RR (95% CI) Died (n = 25) Survived (n = 98) RR (95% CI)

Demographic and clinical characteristics

Age, y, mean (SD) 63.5 (13.1) 60.3 (16.1) 64.1 (14.4) 60. (15.4Male sex 26 (65.0) 59 (71.1) 0.83 (.49–1.40) 15 (60.0) 70 (71.4) 0.67 (.33–1.35)

Weight, kg, mean (SD) 82.7 (30.1) 85.8 (29.8) 81.7 (36.4) 85.5 (28.1)

Recent residence in healthcareinstitution

26 (65.0) 50 (60.2) 1.15 (.67–1.97) 15 (60.0) 61 (62.2) 0.93 (.45–1.89)

Diabetes mellitus 13 (32.5) 41 (49.4) 0.62 (.35–1.07) 9 (36.0) 45 (45.9) 0.72 (.34–1.50)

Heart failure 9 (22.5) 29 (34.9) 0.65 (.34–1.23) 6 (24.0) 32 (32.7) 0.71 (.31–1.63)

COPD 17 (42.5) 22 (26.5) 1.59 (.97–2.62) 10 (40.0) 29 (29.6) 1.44 (.71–2.90)Hepatic dysfunction 2 (5.0) 3 (3.6) 1.24 (.41–3.75) 2 (8.0) 3 (3.1) 2.1 (.66–6.38)

Malignancy 16 (40.0) 18 (21.7) 1.75 (1.06–2.86) 10 (40.0) 24 (24.5) 1.75 (.87–3.50)

Decubitus ulcers 8 (20.0) 19 (22.9) 0.89 (.47–1.7) 5 (20.0) 22 (22.5) 0.89 (.37–2.15)Immunosuppressants 10 (25.0) 13 (15.7) 1.45 (.83–2.52) 6 (24.0) 17 (17.4) 1.37 (.62–3.05)

HIV 1 (2.5) 3 (3.6) 0.76 (.14–4.25) 1 (4.0) 3 (3.1) 1.24 (.22–7.02)

History of cerebrovascular event 2 (5.0) 11 (13.3) 0.45 (.12–1.64) 1 (4.0) 12 (12.2) 0.35 (.05–2.40)Recent surgery in previous 30 d 18 (45.0) 25 (30.1) 1.52 (.92–2.51) 11 (44.0) 32 (32.7) 1.46 (.73–2.94)

Recent antibiotics in previous30 d

21 (52.5) 47 (56.6) 0.89 (.54–1.49) 11 (44.0) 57 (58.2) 0.64 (.31–1.29)

Prior vancomycin in past 30 d 6 (15.0) 16 (19.3) 0.81 (.39–1.69) 3 (12.0) 19 (19.4) 0.63 (.21–1.91)

Length of stay in days prior toindex culture collection,median (IQR)

4 (0–16.75) 0 (0–12) 4 (0–14.5) 0.5 (0–13.2)

Residence in ICU prior to onset 15 (37.5) 27 (32.5) 1.16 (.69–1.94) 10 (40.0) 32 (32.7) 1.29 (.63–2.61)

Intensive care unit at onset 10 (25.0) 20 (24.1) 1.03 (.58–1.86) 7 (28.0) 23 (23.5) 1.21 (.56–2.60)Baseline CrCl, mL/min, mean(SD)

74.7 (52.9) 73.5 (49.9) 84.2 (58.8) 71.3 (48.4)

APACHE-II score, mean (SD) 16.7 (6.4) 12.4 (5.5) 17.7 (6.7) 12.8 (5.6)

APACHE-II score ≥20 13 (32.5) 10 (12.0) 2.09 (1.29–3.39) 9 (36.0) 14 (14.3) 2.45 (1.24–4.82)CDS-ID score at admission,mean (SD)

2.54 (1.86) 2.89 (2.31) 2.40 (1.68) 2.87 (2.28)

Source of bacteremiaIntravenous catheter 17 (42.5) 32 (38.6) 1.12 (.67–1.86) 11 (44.0) 38 (38.8) 1.19 (.59–2.40)

Skin and soft tissue 8 (20.0) 22 (26.5) 0.78 (.40–1.49) 4 (16.0) 26 (26.5) 0.59 (.22–1.58)

Bone and joint 1 (2.5) 7 (8.4) 0.37 (.06–2.35) 1 (4.0) 7 (7.1) 0.60 (.09–3.88)Respiratory tract 2 (5.0) 7 (8.4) 0.67 (.19–2.33) 2 (8.0) 7 (7.1) 1.10 (.31–3.94)

Intra-abdominal 3 (7.5) 5 (6.0) 1.17 (.46–2.96) 2 (8.0) 6 (6.1) 1.25 (.36–4.38)

Urinary tract 0 (0) 2 (2.4) NA 0 (0) 2 (2.0) NAInfected graft/device 3 (7.5) 4 (4.8) 1.34 (.55–3.29) 1 (4.0) 6 (6.1) 0.69 (.11–4.39)

Unknown 6 (15.0) 4 (4.8) 1.99 (1.12–3.56) 4 (16.0) 6 (6.1) 2.15 (.92–5.04)

Infective endocarditis 8 (20.0) 9 (10.8) 1.56 (.87–2.79) 3 (12.0) 14 (14.3) 0.85 (.29–2.53)High-risk source 26 (65.0) 52 (62.7) 1.07 (.63–1.83) 15 (60.0) 63 (64.3) 0.87 (.43–1.76)

Microbiologic phenotypes

MICBMD ≥1 mg/L 10 (25.0) 15 (18.1) 1.31 (.74–2.30) 2 (8.0) 23 (23.5) 0.34 (.09–1.35)MICETEST ≥1.5 mg/L 28 (70.0) 46 (55.4) 1.55 (.87–2.74) 18 (72.0) 56 (57.1) 1.70 (.77–3.77)

MBC/MIC ratio >4 3 (7.5) 4 (4.8) 1.34 (.55–3.29) 3 (12.0) 4 (4.1) 2.26 (.89–5.75)

hVISA 2 (5.0) 2 (2.4) 1.57 (.57–4.32) 0 (0) 4 (4.1) NAAgr dysfunction 18 (45.0) 44 (53.3) 0.81 (.48–1.34) 10 (40.0) 52 (53.1) 0.66 (.32–1.34)

Concomitant antibiotics administered >48 h

Aminoglycosides 10 (25.0) 15 (18.1) 1.31 (.74–2.30) 7 (28.0) 18 (18.4) 1.52 (.72–3.24)Clindamycin 0 (0) 1 (1.2) NA 0 (0) 1 (1.0) NA

Daptomycin 1 (2.5) 1 (1.2) 1.55 (.38–6.35) 0 (0) 2 (2.0) NA

Linezolid 3 (7.5) 11 (13.3) 0.63 (.22–1.78) 2 (8.0) 12 (12.2) 0.68 (.18–2.57)


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variables were concordant in >80% of cases. Given that MICsare largely unknown until day 3 of therapy, these findings sug-gest that clinicians should target daily AUCs that provide ade-quate coverage against the common MIC values observed intheir practices. For most institutions, this will be an MICBMD

of 1 mg/L or MICETEST of 1.5–2 mg/L, which translates intodaily targeted AUC values of 550–650 mg × h/L. Software pro-grams are readily available to estimate AUC based on collectedvancomycin trough levels [22, 43, 44]. Alternatively, vancomy-cin AUCs can be calculated using formulas based on the collec-tion of a few timed measurements [10].As with all observationalstudies, these findings should be interpreted cautiously andneed to be validated in a multicenter randomized trial beforeadoption into practice. However, it is important to note thatthe current “AUC/MIC ratio >400” PK/PD target for vancomy-cin is based on studies [5, 7, 8] with designs similar to this one.

Two additional findings bear mention here. First, no note-worthy relationships between Cmin/MIC and outcomes wereobserved in this study. Although expert guidelines recommendtrough monitoring [1, 2], this was not an unexpected findingfrom an exposure–response perspective. Troughs are a singlepoint estimate of the concentration–time profile at the end of thedosing interval. Whereas a trough ensures the achievement of aminimum cumulative exposure, a wide range of concentration–time profiles can result in a given Cmin [10]. Because cumulativeexposure profiles associated with a given trough value are highlyvariable [10], it is not surprising that Cmin was found to be un-informative and nondiscriminatory. These findings further sup-port the notion that AUC monitoring should be incorporatedinto clinical practice, as it has been identified as the key phar-macodynamic index for vancomycin across a growing numberof in vitro, animal model, and clinical studies [1–8, 45].

Second, whereas the observed vs predicted plots for the Baye-sian approach showed slopes and intercepts very close to theideal values of 1.0 and 0.0, respectively, the formula-based ap-proach did not fit the data well and only explained 35% of the

variance. The poor fit associated with the Cockcroft-Gaultformula best approach does not come as a surprise. This findingwas consistent with older gentamicin PK data, which demon-strated that CrCL-based estimation formulas did not accuratelypredict the observed concentration–time profiles of gentamicin,an antibiotic that is predominately cleared by the kidneys, sim-ilar to vancomycin [9]. This finding further supports the notionthat individualized estimates of exposure profiles based on mea-sured vancomycin concentrations are required when evaluatingvancomycin exposure–response relationships in clinical prac-tice. Interestingly, the formula-based approach underestimatedexposure profiles by approximately 40%–50% (overestimatedclearance by 40%–50%). If one considers that AUC = dose/clearance, our daily AUC target of 550–600 aligns closelywith previous evaluations [5, 7, 8] that noted the criticalAUC/MIC target to be approximately 400.

Several caveats should be noted when interpreting these find-ings. First, this was a study of adult, non-neutropenic, non-dialysis patients. It is unknown if the observed findings areapplicable to other populations. Second, only a limited numberof isolates had MICBMD >1 mg/L. The current paradigm inPK/PD is that a doubling of exposure is required with eachlog2 increase in MIC values. Before we definitively recommenda daily AUC of 1300 for an MICBMD of 2 mg/L, additional re-search is needed. If future studies indicate the pharmacodynamictarget is consistent with the AUC/MICBMD targets observed in thisstudy when the MICBMD is 2 mg/L, it will be difficult to use van-comycin in these instances as the vancomycin AUC needed foreffect will be associated with nephrotoxicity rates >30% [21].

We did not attempt to determine if 30-day mortality was at-tributable to the MRSA BSI. Rather than basing microbiologicalfailure on persistent signs and symptoms of infection, treatmentwas considered a microbiological failure only if the duration ofbacteremia was ≥7 days, as proposed by a number of authors[18, 27, 38]. We believe these aforementioned definitions allowfor an objective assessment of the endpoints and minimize any

Table 4 continued.

Variable Failure (n = 40) Nonfailure (n = 83) RR (95% CI) Died (n = 25) Survived (n = 98) RR (95% CI)

Macrolide 3 (7.5) 2 (2.4) 1.91 (.89–4.12) 3 (12.0) 2 (2.0) 3.22 (1.43–7.23)

Rifampin 3 (7.5) 13 (15.7) 0.54 (.19–1.55) 1 (4.0) 15 (15.3) 0.28 (.04–1.92)Tigecycline 2 (5.0) 1 (1.2) 0.83 (.49–1.40) 1 (4.0) 2 (2.0) 1.67 (.32–8.59)

TMP/SMX 1 (2.5) 8 (9.6) 0.32 (.05–2.10) 0 (0) 9 (9.2) NA

All data are presented as No. (%) unless otherwise noted. Baseline covariates associated with failure at P≤ .2 included APACHE-II score, COPD, diabetes mellitus,malignancy, recent prior surgery, MICETEST ≥1.5 mg/L, and cumulative number of reduced vancomycin susceptibility phenotypes. Baseline covariates associatedwith 30-day mortality at P≤ .2 included APACHE-II score, malignancy, MICETEST ≥1.5 mg/L, MICBMD ≥1 mg/L, and MBC/MIC ratio >4.

Abbreviations: APACHE, Acute Physiology and Chronic Health Evaluation; BMD, broth microdilution; CDS-ID, Chronic Disease Score–Infectious Diseases; CI,confidence interval; Cmin, minimum concentration; COPD, chronic obstructive pulmonary disease; CrCl, creatinine clearance; HIV, human immunodeficiencyvirus; hVISA, heterogeneous vancomycin-intermediate S. aureus; ICU, intensive care unit; IQR, interquartile range; MBC, minimum bactericidal concentration;MIC, minimum inhibitory concentration; NA, not available; RR, relative risk; SD, standard deviation; TMP-SMX, trimethoprim-sulfamethoxazole.


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subjective biases that may result from assessing and interpretingretrospective clinical data. Another consideration in the evalu-ation of MRSA bloodstream infection studies is the adequacy ofsource control. In situations when source control was warrant-ed, there was an attempt to remove the catheter/debride thewound in almost all cases. Among patients who experienced afailure, there was a documented intervention in the medical re-cord to “control the source” in >95% of cases. The frequency ofsource control attempts were not different between CART-derived exposure groups.

In conclusion, our findings suggest that that AUC/MIC, notCmin/MIC, is the pharmacodynamic index most closely linkedto outcomes for patients with MRSA BSIs. Clinicians shouldconservatively target AUC values needed to provide adequateexposure against the common MIC values observed in their in-stitution, given that MICs are largely unknown until therapyday 3. Further research is still needed among patients withMRSA BSIs with vancomycin MICBMD >1 mg/L. As this wasa retrospective observational study, these findings need to bevalidated in a multicenter vancomycin AUC dose-optimizedrandomized outcomes trial before they can be incorporatedinto clinical practice.

Supplementary Data

Supplementary materials are available at Clinical Infectious Diseases online(http://cid.oxfordjournals.org). Supplementary materials consist of dataprovided by the author that are published to benefit the reader. The postedmaterials are not copyedited. The contents of all supplementary data are thesole responsibility of the authors. Questions or messages regarding errorsshould be addressed to the author.

Notes

Acknowledgments. We extend gratitude to Nadia El-Fawal, Jill Butter-field, Benjamin Woo, and Rasha Masoud for data collection and databaseentry, and to Ron Jones, MD, and Rodrigo E. Mendes, PhD, at JMI Labora-tories (North Liberty, Iowa) for characterizing the phenotypic and genotypicprofiles of the MRSA isolates. This article has greatly benefited from thethoughtful editing (grammar and spelling) of Allison Krug, who was paidvia grant funding.Disclaimer. Cubist provided support to complete the project and was

not involved in the design and conduct of the study; collection, manage-ment, analysis, and interpretation of the data; or preparation and reviewof the manuscript.Financial support. This work was supported by an investigator-initiated

research grant from Cubist Pharmaceuticals (principal investigator, T. P. L.).Potential conflicts of interest. T. P. L. is a consultant for Cubist. G. L. D.

is a consultant for Cubist Pharmaceuticals. All other authors report nopotential conflicts.All authors have submitted the ICMJE Form for Disclosure of Potential

Conflicts of Interest. Conflicts that the editors consider relevant to the con-tent of the manuscript have been disclosed.

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