Literature DB >> 26731739

Association between the AUC0-24/MIC Ratio of Vancomycin and Its Clinical Effectiveness: A Systematic Review and Meta-Analysis.

Peng Men1,2, Hui-Bo Li1,2, Suo-Di Zhai1, Rong-Sheng Zhao1.   

Abstract

BACKGROUND: A target AUC0-24/MIC ratio of 400 has been associated with its clinical success when treating Staphylococcus aureus infections but is not currently supported by state-of-the-art evidence-based research.
OBJECTIVE: This current systematic review aimed to evaluate the available evidence for the association between the AUC0-24/MIC ratio of vancomycin and its clinical effectiveness on hospitalized patients and to confirm the existing target value of 400.
METHODS: PubMed, Embase, Web of Sciences, the Cochrane Library and two Chinese literature databases (CNKI, CBM) were systematically searched. Manual searching was also applied. Both RCTs and observational studies comparing the clinical outcomes of high AUC0-24/MIC groups versus low AUC0-24/MIC groups were eligible. Two reviewers independently extracted the data. The primary outcomes were mortality and infection treatment failure. Risk ratios (RRs) with 95% confidence intervals (95%CIs) were calculated.
RESULTS: No RCTs were retrieved. Nine cohort studies were included in the meta-analysis. Mortality rates were significantly lower in high AUC0-24/MIC groups (RR = 0.47, 95%CI = 0.31-0.70, p<0.001). The rates of infection treatment failure were also significantly lower in high AUC/MIC groups and were consistent after correcting for heterogeneity (RR = 0.39, 95%CI = 0.28-0.55, p = 0.001). Subgroup analyses showed that results were consistent whether MIC values were determined by broth microdilution (BMD) method or Etest method. In studies using the BMD method, breakpoints of AUC0-24/MIC all fell within 85% to 115% of 400.
CONCLUSIONS: This meta-analysis demonstrated that achieving a high AUC0-24/MIC of vancomycin could significantly decrease mortality rates by 53% and rates of infection treatment failure by 61%, with 400 being a reasonable target.

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Year:  2016        PMID: 26731739      PMCID: PMC4701440          DOI: 10.1371/journal.pone.0146224

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


Introduction

Vancomycin, a glycopeptide antibiotic, was developed and released in the 1950s for the treatment of aerobic gram-positive infections and has been widely used primarily in the treatment of methicillin-resistant Staphylococcus aureus (MRSA) infections[1]. In recent years, MRSA has spread globally, and the incidence of MRSA has shown a rising trend. Meanwhile, concerns have been raised about inadequate responses to vancomycin if the minimum inhibitory concentration (MIC) of the infecting organism lies at the upper end of the susceptible range[2]. Thus, there is a growing need for monitoring vancomycin levels, evaluating the relationship between its clinical effectiveness and pharmacokinetic parameters, and developing an individualized dosing regimen[3]. Vancomycin appears to follow time-dependent killing[4]. In vitro tests and limited human trials have shown that the ratio of the 24-hour area under the concentration-time curve and MIC (AUC0-24/ MIC) of vancomycin is the pharmacokinetic/pharmacodynamics (PK/PD) parameter that is most relevant to its clinical effectiveness in the treatment of Staphylococcus aureus infections[5,6]. An AUC0-24/MIC ratio higher than 400 is regarded as the target of clinical success. However, this conclusion was based on only one clinical observational study, which used the broth microdilution (BMD) method to test MIC, and had not been confirmedby performing a systematic review. Moreover, little was known regarding to what extent achieving high AUC0-24/MIC levels of vancomycin could enhance its clinical effectiveness. Thus, we performed a systematic review and meta-analysis to study these issues.

Methods

Search strategy

Previously published articles and conference abstracts (until Jan 16, 2014) reporting the association between the AUC0-24/MIC ratio of vancomycin and its clinical effectiveness were identified by computerized literature searches in PubMed, Embase, the Web of Science, the Cochrane Library and two Chinese literature databases (CNKI and CBM). Reference lists of the retrieved articles and supplemental materials were also examined manually to further identify potentially relevant studies. The search term was “vancomycin”. No restriction on language was applied. This study was a part of the development of Chinese practice guideline for therapeutic drug monitoring (TDM) of vancomycin launched by the Division of Therapeutic Drug Monitoring, Chinese Pharmacological Society.

Selection criteria

The literature was divided into eight parts whose eligibility was assessed according to titles and abstracts by eight separate groups based on PICOs relating to the development of Chinese practice guideline for vancomycin TDM (See S1 File for details). Each group consisted of two independent assessors. Results of the assessment were converged as a whole. Furthermore, two authors (PM and HBL) independently searched the literature and examined the relevant studies for further assessment of the data. Each reviewer was blinded to the other reviewer during the process of data extraction. In cases of disagreement between the two reviewers, a third author (SDZ) was consulted. Both randomized controlled trials (RCTs) and observational studies were eligible with the following inclusion criteria: 1) comparing clinical effectiveness of high and low AUC0-24/MIC levels of vancomycin monotherapy; 2) breakpoints of the AUC0-24/MIC ratio were reported; 3) patients were diagnosed with gram-positive infections with well-validated diagnostic criteria. Reviews, editorials, guidelines and case reports were excluded. We also contacted the authors for related information if data provided were insufficient.

Data extraction and outcomes

All data were extracted independently by the two authors. Data extracted from the identified studies included the author (s), year of publication, country in which the study was conducted, study design, number of patients enrolled, population characteristics (type and etiology of infections), results of clinical outcomes and breakpoints of AUC0-24/MIC ratio. The outcomes of the review were all-cause mortality and infection treatment failure.

Quality appraisal

Two authors independently assessed the quality of included studies. Discrepancies were resolved by discussion or through consultation with the third reviewer (SDZ). The potential risks of bias in RCTs were assessed according to the criteria developed using the Cochrane risk of bias tool. The quality of observational studies was assessed using the New Castle-Ottawa (NOS) scales.

Statistical analysis

All statistical analyses were performed in STATA 12.0 (Stata Corp LP, College Station, TX, United States). Pooled risk ratios (RRs) and 95% confidence intervals (CIs) were calculated using the Mantel-Haenszel (M-H) fixed effects model if there was no evidence of significant heterogeneity for these outcomes, or random effects model if significant heterogeneity for these outcomes was present. Subgroup analyses were performed according to different methods for MIC determination. Heterogeneity among the studies was assessed using χ2 test and quantified using the Higgins I2[7]. To account for the low statistical power of the χ2 test for heterogeneity, P = 0.10 was considered not significant. If P<0.10, then a sensitivity analysis was performed to assess the validity of the outcomes. Publication bias was examined by Egger’s tests if there were at least five studies for each outcome[8]. The significance level was set at 0.05. Methods for this systematic review were developed according to recommendations from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklists (See S2 File for details).

Results

A total of 66464 were excluded from 67406 references after a review of titles: 21621 were duplicate and 44843 were not relevant to the guideline drafting. Next, we initially identified 942 potentially relevant studies using systematic database searching and 13 studies using manual searching. The full-text articles of the remaining 955 studies were evaluated. Another 938 studies were excluded because they were not relevant to this systematic review. Among the other 17 studies, 5 of the studies did not compare the clinical outcomes between the high and low AUC0-24/MIC ratio groups, 3 studies did not present sufficient clinical data. Nine studies were ultimately included in the meta-analysis. The flow chart of selection of studies and reasons for exclusion is presented in Fig 1.
Fig 1

Flow chart depicting the selection process of studies included in the meta-analysis.

Study description

Studies included in our review were all cohort studies[9-17]. No RCTs were retrieved. These studies included a total of 916 patients. A summary description of the included studies is reported in Table 1. Of these studies, four studies were performed in the United States, two in Australia, and one each in Belgium, Canada, and the Republic of Korea.
Table 1

Main Characteristics of Studies Included in the Meta-analysis.

Reference (year)Study design; countryNumber of patientsPathogensType of infectionsMale,%Mean age(SD)
Ampe et al.(2013) [9]Prospective; Belgium20CoNS, MRSA, MSSAForeign body, osteomyelitis, septicaemia7065.6(12.6)
Brown et al.(2012)[10]Retrospective; the United States44MRSAComplicated bacteremia, infective endocarditis5054.8(16)
Gawronski et al.(2013)[11]Retrospective; the United States59MRSAComplicated bacteria, osteomyelitis5954(16)
Ghosh et al.(2014)[12]Retrospective; Australia127MRSAAbdominal sources, endocarditis, non-endocarditis vascular sources, pneumonia68.564.6(NR)
Holmes et al.(2013)[13]Prospectivea; Australia182MRSA, MSSAEndocarditis, osteoarticular, pneumonia, sepsis syndrome, skin and soft tissue70NR
Jung et al.(2014)[14]Retrospective; the Republic of Korea76MRSABone and joint, catheter-related, deep incisional/organ space, endocarditis, pneumonia, skin and soft tissue, surgical site76.3NR
Kullar et al.(2011)[15]Retrospective; the United States320MRSABone and joint, catheter-related, deep abscess, endocarditis, multiple sites, pneumonia, skin/woundNR54(NR)
Moise et al.(2000)[16]Retrospective; the United States53MRSA, MSSALower respiratory tract6169.1(15)
Zelenitsky et al.(2013)[17]Retrospective; Canada35MRSABloodstream, central nervous system, endocarditis, intra-abdominal, lower respiratory tract, skin/skin structure62.961.9(15.2)

CoNS, coagulase-negative Staphylococci; MSSA, methicillin sensitive Staphylococcus aureus; NR, not reported; SD, standard difference.

a Additional clinical data required for analysis were collected retrospectively using a detailed chart review.

CoNS, coagulase-negative Staphylococci; MSSA, methicillin sensitive Staphylococcus aureus; NR, not reported; SD, standard difference. a Additional clinical data required for analysis were collected retrospectively using a detailed chart review.

Quality of the included studies

Quality appraisal of the included cohort studies is shown in Table 2. Six studies completely accounted for the nine factors assessed. One study was adequate in eight of the nine factors, and two studies were adequate in seven of the nine factors.
Table 2

Quality Appraisal of Observational Studies.

References(year)Quality indicators
1a2b3c4d5Ae5Bf6g7h8i
Ampe et al.(2013)YesYesYesYesYesYesYesYesYes
Brown et al.(2012)YesYesYesYesYesYesYesYesYes
Gawronski et al.(2013)YesYesYesYes//YesYesYes
Ghosh et al.(2014)YesYesYesYesYesYesYesYesYes
Holmes et al.(2013)YesYesYesYesNoYesYesYesYes
Jung et al.(2014)YesYesYesYesYesYesYesYesYes
Kullar et al.(2011)YesYesYesYesYesYesYesYesYes
Moise et al.(2000)YesYesYesYesYesYesYesYesYes
Zelenitsky et al.(2013)YesYesYesYesNoNoYesYesYes

a Indicates exposed cohort truly representative.

b Non-exposed cohort drawn from the same community.

c Ascertainment of exposure from a secure record.

d Outcome of interest not present at start of study.

e Cohorts comparable on basis of site and etiology of infections, or APACHEII Score.

f Cohorts comparable on other factors.

g Assessment of outcome of record linkage or independent blind assessment.

h Follow-ups that were sufficient for outcomes to occur.

i Complete accounting for cohorts.

a Indicates exposed cohort truly representative. b Non-exposed cohort drawn from the same community. c Ascertainment of exposure from a secure record. d Outcome of interest not present at start of study. e Cohorts comparable on basis of site and etiology of infections, or APACHEII Score. f Cohorts comparable on other factors. g Assessment of outcome of record linkage or independent blind assessment. h Follow-ups that were sufficient for outcomes to occur. i Complete accounting for cohorts.

Outcomes

Mortality

Four studies reported all-cause mortality. There was no significant heterogeneity among these studies (p = 0.785,I2 = 0.0%), so a fixed effects model was used (Fig 2). Compared with low AUC0-24/MIC groups, the high AUC0-24/MIC groups had significantly lower mortality rates (RR = 0.47, 95%CI = 0.31–0.70, p<0.001).
Fig 2

Risk ratios of all-cause mortality rates: high versus low AUC0-24/MIC ratio.

Test of all-cause mortality rates for overall effect: Z = 3.72, P<0.001; test of all-cause mortality rates in Etest Study subgroup for overall effect: Z = 2.12, P = 0.034; test of all-cause mortality rates in BMD Study subgroup for overall effect: Z = 3.12, P = 0.002.

Risk ratios of all-cause mortality rates: high versus low AUC0-24/MIC ratio.

Test of all-cause mortality rates for overall effect: Z = 3.72, P<0.001; test of all-cause mortality rates in Etest Study subgroup for overall effect: Z = 2.12, P = 0.034; test of all-cause mortality rates in BMD Study subgroup for overall effect: Z = 3.12, P = 0.002. Among these studies, two studies applied the BMD method (referred to as the “BMD Study”), and the remaining studies applied the Etest method (referred to as the “Etest Study”) for MIC determination. Subgroup analyses showed that in both the BMD Study and Etest Study, high AUC0-24/MIC groups had significantly lower mortality rates (BMD: RR = 0.49, 95%CI = 0.31–0.77, p = 0.002; Etest: RR = 0.37, 95%CI = 0.15–0.93, p = 0.034). No significant heterogeneity was found (BMD: p = 0.395, I2 = 0.0% and Etest: p = 0.817,I2 = 0.0%).

Rate of infection treatment failure

Six studies reported rates of infection treatment failure. There was significant heterogeneity among these studies (p = 0.014, I2 = 64.7%), and thus we used a random-effect model (Fig 3). Compared with low AUC0-24/MIC groups, high AUC0-24/MIC groups had significantly lower rates of infection treatment failure (RR = 0.47, 95%CI = 0.30–0.73, p = 0.001). These six studies applied the BMD method to determine the MIC values.
Fig 3

Risk ratios of rates of infection treatment failure: high versus low AUC0-24/MIC ratio.

Test of rates of infection treatment failure for overall effect: Z = 3.34, P = 0.001.

Risk ratios of rates of infection treatment failure: high versus low AUC0-24/MIC ratio.

Test of rates of infection treatment failure for overall effect: Z = 3.34, P = 0.001.

Breakpoints of the AUC0-24/MIC ratio

Among the studies included in this systematic review, seven studies applied BMD method to determine the MIC values. All breakpoints reported from these studies fell within 85% to 115% of 400, ranging from 345 to 451 (See Table 3).
Table 3

Outcomes, breakpoints of AUC0-24/MIC ratio and MIC determination methods of studies included in the meta-analysis.

Reference(year)Outcomes available in systematic reviewAll-cause mortality (deaths/total)Infection treatment failure (failures/total)Breakpoints of AUC0-24/MIC ratioMIC determination methods
Higher than breakpointLower than breakpointHigher than breakpointLower than breakpoint
Ampe et al.(2013)Infection treatment failure//2/143/6451BMD
Brown et al.(2012)All-cause mortality6/376/7//211Etest
Gawronski et al.(2013)All-cause mortality; Infection treatment failure0/231/362/237/36293Etest
Ghosh et al.(2014)Infection treatment failure//18/7727/50398BMD
Holmes et al.(2013)All-cause mortality17/10821/74//373BMD
Jung et al.(2014)Infection treatment failure//10/5210/24398.5BMD
Kullar et al.(2011)Infection treatment failure//107/22161/99421BMD
Moise et al.(2000)Infection treatment failure//7/3216/21345BMD
Zelenitsky et al.(2013)All-cause mortality6/2012/15//451BMD

Heterogeneity and publication bias

Because there was significant heterogeneity among the studies reporting rates of infection treatment failure, we performed a sensitivity analysis (See S1 Fig), which showed that Kullar’s study had a significant effect on the meta-analysis result. Heterogeneity was not significant after we excluded that study (I2 = 0.0%, p = 0.858) and results were consistent (See Fig 4, RR = 0.39, 95%CI = 0.28–0.55, p = 0.001).
Fig 4

Risk ratios of rates of infection treatment failure: high versus low AUC0-24/MIC ratio (Kullar’s study was excluded).

Test of rates of infection treatment failure for overall effect: Z = 5.55, P<0.001.

Risk ratios of rates of infection treatment failure: high versus low AUC0-24/MIC ratio (Kullar’s study was excluded).

Test of rates of infection treatment failure for overall effect: Z = 5.55, P<0.001. For rates of infection treatment failure, the amount of studies was enough to perform the Egger’s test for publication bias. No significant result was observed (P = 0.706).

Discussion

Our systematic review and meta-analysis demonstrated that high values of AUC0-24/MIC had significant advantages compared to low values in terms of all-cause mortality rates (reduced by 53%) and rates of infection treatment failure (reduced by 53%, coming to 61% after correcting for heterogeneity) by reaching the breakpoint of 400. To the best of our knowledge, this report describes the first systematic review to support this hypothesis. Our research has raised the grade of evidence concerning this issue, which may be beneficial for clinicians, clinical pharmacists and future therapeutic drug monitoring guideline writers. Kullar’s study was excluded from meta-analysis because of its significant effect on heterogeneity. The possible reason of the effect was that patients among the cohort had relatively higher baseline APACHE-II scores. Importantly, the apparent values of AUC0-24/ MIC ratio vary depending on different MIC determination methods. Commonly applied methods include the BMD and Etest methods. Latter results are consistently 0.5 to 1.5 times higher than the former results after log2 conversion[18,19]. A target value of 400 recommended by the American guideline was based on results obtained from BMD method[6]. Among the studies included in this meta-analysis, 7 studies applied BMD method. Breakpoints of the AUC0-24/MIC ratio were entirely within the range of 85–115% of 400. In addition, because the breakpoints were calculated using a fine classification and regression tree (CART) algorithm and a margin of error may exists, there will be slight differences. Different infection sites and pathogens may also cause the variations, although apparent associations were difficult to be found based on included studies. Our study had confirmed the target AUC0-24/MIC value of 400. Two studies reported 211 and 293 as breakpoints respectively. The Etest method was applied for MIC determination, which probably accounting for the lower values. Holmes et al.[13] had performed a linear regression analysis and found that the target of 400 (based on BMD method) was equivalent to 226 based on the Etest method, which was very close to 211. This provided evidence for our analysis. Pharmacokinetic and pharmacodynamics reviews have recommended the AUC0-24/MIC ratio as the preferred parameter partly based on data obtained from animal models, in vitro studies and limited human studies[20-23]. Moise-Broder et al. explored the use of AUC0-24/MIC ratio in predicting clinical and microbiological success in the treatment of ventilator-associated S. aureus pneumonia. An AUC0-24/MIC ratio of ≥ 400 was advocated as a target to achieve clinical success with vancomycin[6]. On the basis of these studies, the vancomycin TDM guideline (published in 2009) developed by American Society of Hospital-System Pharmacists recommended 400 as a clinically target, which requiring further support from solid evidence-based research. However, there are some deficiencies in this study. First, a relatively small number of studies were included, which were all observational ones. Second, there was significant heterogeneity among the six studies, which reported the rate of infection treatment failure. After we found and excluded the questionable study, the problem was resolved and meta-analysis results were consistent. This study focused on the relevant predictors of vancomycin clinical effectiveness. At the present, researches on the association between vancomycin AUC0-24/ MIC and safety are limited. Some studies showed that high ratio values may increase the incidence of nephrotoxicity[18]. Neely et al.[24] discovered that when the AUC0-24/ MIC ratio was higher than 700, the incidence of nephrotoxicity was significantly increased, based on a population model. Lodise et al. [25] found that in 27 patients whose AUC0-24/ MIC values were equal to or higher than 1300, 26% had developed nephrotoxicity. Additional studies on nephrotoxicity are needed in the future. In conclusion, this report describes the first systematic review indicating that achieving a high AUC0-24/MIC ratio of vancomycin could significantly decrease mortality rates by 53% and rates of infection treatment failure by 61%, with 400 being a reasonable target.

Sensitivity analysis of the studies reporting the rate of infection treatment failure.

(TIF) Click here for additional data file.

A protocol of Chinese practice guideline for therapeutic drug monitoring of vancomycin (Including details of the first step of literatures selection).

(PDF) Click here for additional data file.

PRISMA 2009 Checklist.

(DOC) Click here for additional data file.

List of full-text excluded articles and reasons for exclusion.

(DOCX) Click here for additional data file.
  25 in total

Review 1.  The pharmacokinetic and pharmacodynamic properties of vancomycin.

Authors:  Michael J Rybak
Journal:  Clin Infect Dis       Date:  2006-01-01       Impact factor: 9.079

Review 2.  Therapeutic monitoring of vancomycin in adult patients: a consensus review of the American Society of Health-System Pharmacists, the Infectious Diseases Society of America, and the Society of Infectious Diseases Pharmacists.

Authors:  Michael Rybak; Ben Lomaestro; John C Rotschafer; Robert Moellering; William Craig; Marianne Billeter; Joseph R Dalovisio; Donald P Levine
Journal:  Am J Health Syst Pharm       Date:  2009-01-01       Impact factor: 2.637

3.  Bias in meta-analysis detected by a simple, graphical test.

Authors:  M Egger; G Davey Smith; M Schneider; C Minder
Journal:  BMJ       Date:  1997-09-13

4.  Area under the inhibitory curve and a pneumonia scoring system for predicting outcomes of vancomycin therapy for respiratory infections by Staphylococcus aureus.

Authors:  P A Moise; A Forrest; S M Bhavnani; M C Birmingham; J J Schentag
Journal:  Am J Health Syst Pharm       Date:  2000-10-15       Impact factor: 2.637

Review 5.  Pharmacokinetic/pharmacodynamic parameters: rationale for antibacterial dosing of mice and men.

Authors:  W A Craig
Journal:  Clin Infect Dis       Date:  1998-01       Impact factor: 9.079

6.  Pharmacodynamics of vancomycin and other antimicrobials in patients with Staphylococcus aureus lower respiratory tract infections.

Authors:  Pamela A Moise-Broder; Alan Forrest; Mary C Birmingham; Jerome J Schentag
Journal:  Clin Pharmacokinet       Date:  2004       Impact factor: 6.447

Review 7.  Basic pharmacodynamics of antibacterials with clinical applications to the use of beta-lactams, glycopeptides, and linezolid.

Authors:  William A Craig
Journal:  Infect Dis Clin North Am       Date:  2003-09       Impact factor: 5.982

8.  Impact of source of infection and vancomycin AUC0-24/MICBMD targets on treatment failure in patients with methicillin-resistant Staphylococcus aureus bacteraemia.

Authors:  N Ghosh; R Chavada; M Maley; S J van Hal
Journal:  Clin Microbiol Infect       Date:  2014-07-12       Impact factor: 8.067

9.  In vitro studies of pharmacodynamic properties of vancomycin against Staphylococcus aureus and Staphylococcus epidermidis.

Authors:  E Löwdin; I Odenholt; O Cars
Journal:  Antimicrob Agents Chemother       Date:  1998-10       Impact factor: 5.191

10.  Analysis of vancomycin time-kill studies with Staphylococcus species by using a curve stripping program to describe the relationship between concentration and pharmacodynamic response.

Authors:  B H Ackerman; A M Vannier; E B Eudy
Journal:  Antimicrob Agents Chemother       Date:  1992-08       Impact factor: 5.191

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  21 in total

1.  Performance of Area under the Concentration-Time Curve Estimations of Vancomycin with Limited Sampling by a Newly Developed Web Application.

Authors:  Kazutaka Oda; Yumi Hashiguchi; Toshimi Kimura; Yasuhiro Tsuji; Kensuke Shoji; Yoshiko Takahashi; Kazuaki Matsumoto; Hideki Kawamura; Hideyuki Saito; Yoshio Takesue
Journal:  Pharm Res       Date:  2021-03-29       Impact factor: 4.200

2.  Pharmacokinetics of and maintenance dose recommendations for vancomycin in severe pneumonia patients undergoing continuous venovenous hemofiltration with the combination of predilution and postdilution.

Authors:  Qiang Li; Fenghua Liang; Ling Sang; Pengpeng Li; Bijun Lv; Lu Tan; Xiaoqing Liu; Wenying Chen
Journal:  Eur J Clin Pharmacol       Date:  2019-11-16       Impact factor: 2.953

3.  [Effect of augmented renal clearance on plasma concentration of vancomycin and treatment outcome in children with methicillin-resistant Staphylococcus aureus infection].

Authors:  Cui-Yao He; Yan-Ran Qin; Cheng-Jun Liu; Jie Ren; Ji-Shan Fan
Journal:  Zhongguo Dang Dai Er Ke Za Zhi       Date:  2019-09

4.  Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016.

Authors:  Andrew Rhodes; Laura E Evans; Waleed Alhazzani; Mitchell M Levy; Massimo Antonelli; Ricard Ferrer; Anand Kumar; Jonathan E Sevransky; Charles L Sprung; Mark E Nunnally; Bram Rochwerg; Gordon D Rubenfeld; Derek C Angus; Djillali Annane; Richard J Beale; Geoffrey J Bellinghan; Gordon R Bernard; Jean-Daniel Chiche; Craig Coopersmith; Daniel P De Backer; Craig J French; Seitaro Fujishima; Herwig Gerlach; Jorge Luis Hidalgo; Steven M Hollenberg; Alan E Jones; Dilip R Karnad; Ruth M Kleinpell; Younsuk Koh; Thiago Costa Lisboa; Flavia R Machado; John J Marini; John C Marshall; John E Mazuski; Lauralyn A McIntyre; Anthony S McLean; Sangeeta Mehta; Rui P Moreno; John Myburgh; Paolo Navalesi; Osamu Nishida; Tiffany M Osborn; Anders Perner; Colleen M Plunkett; Marco Ranieri; Christa A Schorr; Maureen A Seckel; Christopher W Seymour; Lisa Shieh; Khalid A Shukri; Steven Q Simpson; Mervyn Singer; B Taylor Thompson; Sean R Townsend; Thomas Van der Poll; Jean-Louis Vincent; W Joost Wiersinga; Janice L Zimmerman; R Phillip Dellinger
Journal:  Intensive Care Med       Date:  2017-01-18       Impact factor: 17.440

5.  Vancomycin dosing and therapeutic drug monitoring practices: guidelines versus real-life.

Authors:  Tatjana Van Der Heggen; Franky M Buyle; Barbara Claus; Annemie Somers; Petra Schelstraete; Peter De Paepe; Sophie Vanhaesebrouck; Pieter A J G De Cock
Journal:  Int J Clin Pharm       Date:  2021-04-28

6.  Early Bayesian Dose Adjustment of Vancomycin Continuous Infusion in Children: a Randomized Controlled Trial.

Authors:  Romain Berthaud; Sihem Benaboud; Déborah Hirt; Mathieu Genuini; Mehdi Oualha; Martin Castelle; Coralie Briand; Solène Artru; Lorenzo Norsa; Olivia Boyer; Frantz Foissac; Naïm Bouazza; Jean-Marc Tréluyer
Journal:  Antimicrob Agents Chemother       Date:  2019-10-07       Impact factor: 5.191

7.  Point-Counterpoint: Should Clinical Microbiology Laboratories Report Vancomycin MICs?

Authors:  Sara L Revolinski; Christopher D Doern
Journal:  J Clin Microbiol       Date:  2021-03-19       Impact factor: 5.948

Review 8.  Bacterial sepsis : Diagnostics and calculated antibiotic therapy.

Authors:  D C Richter; A Heininger; T Brenner; M Hochreiter; M Bernhard; J Briegel; S Dubler; B Grabein; A Hecker; W A Kruger; K Mayer; M W Pletz; D Storzinger; N Pinder; T Hoppe-Tichy; S Weiterer; S Zimmermann; A Brinkmann; M A Weigand; C Lichtenstern
Journal:  Anaesthesist       Date:  2019-02       Impact factor: 1.041

Review 9.  [Bacterial sepsis : Diagnostics and calculated antibiotic therapy].

Authors:  D C Richter; A Heininger; T Brenner; M Hochreiter; M Bernhard; J Briegel; S Dubler; B Grabein; A Hecker; W A Krüger; K Mayer; M W Pletz; D Störzinger; N Pinder; T Hoppe-Tichy; S Weiterer; S Zimmermann; A Brinkmann; M A Weigand; Christoph Lichtenstern
Journal:  Anaesthesist       Date:  2017-10       Impact factor: 1.041

10.  Clinical Guideline Highlights for the Hospitalist: Therapeutic Monitoring of Vancomycin.

Authors:  Mark Murphy; Sonya Tang Girdwood; Marc H Scheetz
Journal:  J Hosp Med       Date:  2020-12       Impact factor: 2.960

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