Dataset Information

Vancomycin Area Under the Curve and Acute Kidney Injury: A Meta-analysis.

ABSTRACT: BACKGROUND:This study analyzed the relationship between vancomycin area under the concentration-time curve (AUC) and acute kidney injury (AKI) reported across recent studies. METHODS:A systematic review of PubMed, Medline, Scopus, and compiled references was conducted. We included randomized cohort and case-control studies that reported vancomycin AUCs and risk of AKI (from 1990 to 2018). The primary outcome was AKI, defined as an increase in serum creatinine of ?0.5 mg/L or a 50% increase from baseline on ?2 consecutive measurements. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. Primary analyses compared the impact of AUC cutpoint (greater than ~650 mg × hour/L) and AKI. Additional analysis compared AUC vs trough-guided monitoring on AKI incidence. RESULTS:Eight observational studies met inclusion/exclusion criteria with data for 2491 patients. Five studies reported first-24-hour AUCs (AUC0-24) and AKI, 2 studies reported 24- to 48-hour AUCs (AUC24-48) and AKI, and 2 studies reported AKI associated with AUC- vs trough-guided monitoring. AUC less than approximately 650 mg × hour/L was associated with decreased AKI for AUC0-24 (OR, 0.36 [95% CI, .23-.56]) as well as AUC24-48 (OR, 0.45 [95% CI, .27-.75]). AKI associated with the AUC monitoring strategy was significantly lower than trough-guided monitoring (OR, 0.68 [95% CI, .46-.99]). CONCLUSIONS:AUCs measured in the first or second 24 hours and lower than approximately 650 mg × hour/L may result in a decreased risk of AKI. Vancomycin AUC monitoring strategy may result in less vancomycin-associated AKI. Additional investigations are warranted.

SUBMITTER: Aljefri DM

PROVIDER: S-EPMC6853683 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Vancomycin Area Under the Curve and Acute Kidney Injury: A Meta-analysis.

Aljefri Doaa M DM Avedissian Sean N SN Rhodes Nathaniel J NJ Postelnick Michael J MJ Nguyen Kevin K Scheetz Marc H MH

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 20191101 11

<h4>Background</h4>This study analyzed the relationship between vancomycin area under the concentration-time curve (AUC) and acute kidney injury (AKI) reported across recent studies.<h4>Methods</h4>A systematic review of PubMed, Medline, Scopus, and compiled references was conducted. We included randomized cohort and case-control studies that reported vancomycin AUCs and risk of AKI (from 1990 to 2018). The primary outcome was AKI, defined as an increase in serum creatinine of ≥0.5 mg/L or a 50% ...[more]

PMID: 30715208

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Project description:ObjectivesTo assess differences in vancomycin AUC estimates from two common, clinically applied first-order pharmacokinetic equation methods compared with Bayesian estimates.MethodsA cohort of patients who received vancomycin and therapeutic drug monitoring was studied. First-order population pharmacokinetic equations were used to guide initial empirical dosing. After receipt of the first dose, patients had peak and trough serum levels drawn and steady-state AUC was estimated using first-order pharmacokinetic equations as standard care. We subsequently created a Bayesian model and used individual Empirical Bayes Estimates to precisely calculate vancomycin AUC24-48, AUC48-72 and AUC72-96 in this cohort. AUC at steady state (AUCSS) differences from the first-order methods were compared numerically and categorically (i.e. below, within or above 400-600 mg·h/L) to Bayesian AUCs, which served as the gold standard.ResultsA total of 65 adult inpatients with 409 plasma samples were included in this analysis. A two-compartment intravenous infusion model with first-order elimination fit the data well. The mean of Bayesian AUC24-48 was not significantly different from AUC estimates from the two first-order pharmacokinetic equation methods (P = 0.68); however, Bayesian AUC48-72 and Bayesian AUC72-96 were both significantly different when compared with both first-order pharmacokinetic equation methods (P < 0.01 for each). At the patient level, categorical classifications of AUC estimates from the two first-order pharmacokinetic equation methods differed from categorizations derived from the Bayesian calculations. Categorical agreement was ∼50% between first-order and Bayesian calculations, with declining categorical agreement observed with longer treatment courses. Differences in categorical agreement between calculation methods could potentially result in different dose recommendations for the patient.ConclusionsBayesian-calculated AUCs between 48-72 and 72-96 h intervals were significantly different from first-order pharmacokinetic method-estimated AUCs at steady state. The various calculation methods resulted in different categorical classification, which could potentially lead to erroneous dosing adjustments in approximately half of the patients.

Project description:INTRODUCTION:Vancomycin associated acute kidney injury (vAKI) is a well known complication in pediatric patients. Identification and characterization of the incidence and risk factors for vAKI in the pediatric population would assist clinicians in potentially preventing or mitigating vAKI. METHODS AND MATERIALS:A 6 year retrospective cohort study was designed. Patients were included if they were < 19 years of age, received vancomycin as inpatients, and had a baseline SCr and one other SCr drawn during and up to 72 hours after the discontinuation of vancomycin. Data collection included patient demographics, vancomycin doses and length of therapy, vancomycin serum concentrations, and concomitant medications. The Kidney Disease Improving Global Outcomes (KDIGO) criteria were used to characterize acute kidney injury. Descriptive statistical methods were used and ordinal logistic regression was employed to determine variables significantly associated with vAKI. RESULTS:A total of 7,095 patients met study criteria (55.4% male, median age 4.1 years (IQR 0.67-11.2 years)). Mechanical ventilation was used in 7.9% (n = 563) and mortality was 4.9% (n = 344). A total of 153 concomitant medications were identified. A median of 5 (IQR 3-7) SCr values were obtained and median SCr prior to vancomycin was 0.39 (IQR 0.28-0.57) mg/dL (CrCl 134±58 mL/min/1.73m2). Vancomycin was administered for a median of 2 (IQR 1-3) days (14.9±1.6 mg/kg/dose). vAKI was present in 12.2% (n = 862: KDIGO stage 1 (8.30%, n = 589), KDIGO stage 2 (1.94%, n = 138) KDIGO stage 3 (1.89%, n = 134)). Mean vancomycin serum concentration at 6-8 hours after a dose for patients with vAKI (10.7±8.9 mg/L) was significantly, but not clinically different for patients with no vAKI (7.5±6.3 mg/L). (p<0.05) Ordinal logistic regression identified total dose of vancomycin, vancomycin administration in the intensive care unit, and concomitant medication administration as significant for vAKI. In particular, concomitant administration of several different medications, including nafcillin, clindamycin, and acetazolamide, were noted for strong associations with vAKI. (p<0.05). CONCLUSIONS:Moderate to severe acute kidney injury due to vancomycin is infrequent in children and associated with concomitant medication use and total dose of vancomycin. Serum vancomycin concentrations are not useful predictors of vAKI in the pediatric population.

Project description:Evidence suggests that maintenance of vancomycin trough concentrations at between 15 and 20 mg/liter, as currently recommended, is frequently unnecessary to achieve the daily area under the concentration-time curve (AUC24) target of ?400 mg · h/liter. Many patients with trough concentrations in this range have AUC24 values in excess of the therapeutic threshold and within the exposure range associated with nephrotoxicity. On the basis of this, the Detroit Medical Center switched from trough concentration-guided dosing to AUC-guided dosing to minimize potentially unnecessary vancomycin exposure. The primary objective of this analysis was to assess the impact of this intervention on vancomycin-associated nephrotoxicity in a single-center, retrospective quasi-experiment of hospitalized adult patients receiving intravenous vancomycin from 2014 to 2015. The primary analysis compared the incidence of nephrotoxicity between patients monitored by assessment of the AUC24 and those monitored by assessment of the trough concentration. Multivariable logistic and Cox proportional hazards regression examined the independent association between the monitoring strategy and nephrotoxicity. Secondary analysis compared vancomycin exposures (total daily dose, AUC, and trough concentrations) between monitoring strategies. Overall, 1,280 patients were included in the analysis. After adjusting for severity of illness, comorbidity, duration of vancomycin therapy, and concomitant receipt of nephrotoxins, AUC-guided dosing was independently associated with lower nephrotoxicity by both logistic regression (odds ratio, 0.52; 95% confidence interval [CI], 0.34 to 0.80; P = 0.003) and Cox proportional hazards regression (hazard ratio, 0.53; 95% CI, 0.35 to 0.78; P = 0.002). AUC-guided dosing was associated with lower total daily vancomycin doses, AUC values, and trough concentrations. Vancomycin AUC-guided dosing was associated with reduced nephrotoxicity, which appeared to be a result of reduced vancomycin exposure.

Project description:BackgroundRisk prediction models have been proposed for various diseases and are being improved as new predictors are identified. A major challenge is to determine whether the newly discovered predictors improve risk prediction. Decision curve analysis has been proposed as an alternative to the area under the curve and net reclassification index to evaluate the performance of prediction models in clinical scenarios. The decision curve computed using the net benefit can evaluate the predictive performance of risk models at a given or range of threshold probabilities. However, when the decision curves for 2 competing models cross in the range of interest, it is difficult to identify the best model as there is no readily available summary measure for evaluating the predictive performance. The key deterrent for using simple measures such as the area under the net benefit curve is the assumption that the threshold probabilities are uniformly distributed among patients.MethodsWe propose a novel measure for performing decision curve analysis. The approach estimates the distribution of threshold probabilities without the need of additional data. Using the estimated distribution of threshold probabilities, the weighted area under the net benefit curve serves as the summary measure to compare risk prediction models in a range of interest.ResultsWe compared 3 different approaches, the standard method, the area under the net benefit curve, and the weighted area under the net benefit curve. Type 1 error and power comparisons demonstrate that the weighted area under the net benefit curve has higher power compared to the other methods. Several simulation studies are presented to demonstrate the improvement in model comparison using the weighted area under the net benefit curve compared to the standard method.ConclusionsThe proposed measure improves decision curve analysis by using the weighted area under the curve and thereby improves the power of the decision curve analysis to compare risk prediction models in a clinical scenario.

Project description:IntroductionConsensus guidelines recommend targeting a vancomycin area under the curve to minimum inhibitory concentration (AUC24 :MIC) ratio of 400-600 to improve therapeutic success and reduce nephrotoxicity. Although guidelines specify either Bayesian software or first-order equations may be used to estimate AUC24 , there are currently no large studies directly comparing these methods.ObjectiveTo compare calculated vancomycin AUC24 using first-order equations with two-drug concentrations at steady state to Bayesian two- and one-concentration estimations.MethodsThis was a single-center, retrospective cohort study of 978 adult hospitalized patients receiving intravenous vancomycin between 2017 and 2019. Patients were included if they received at least 72 h of vancomycin and had two-serum drug concentrations obtained. AUC24 was calculated using first-order analytic (linear), Bayesian two-concentration, and Bayesian one-concentration methods for each patient. The InsightRx™ software platform was used to calculate Bayesian AUC24 . Pearson's correlation and clinical agreement (based on AUC24 classified as subtherapeutic, therapeutic, or supratherapeutic) were used to assess agreement between methods. Bland-Altman plots were used to assess mean difference (MD) and 95% limits of agreement (LOA).ResultsExcellent agreement was observed between linear and Bayesian two-concentration methods (r = 0.963, clinical agreement = 87.4%) and Bayesian two-concentration and one-concentration methods (r = 0.931, clinical agreement = 88.5%); however, a degree of variability was noted with 95% LOA -99 to 76 (MD = -11.5 mg*h/L) and -92 to 113 (MD = -10.4 mg*h/L), for the respective comparisons. The agreement between linear and Bayesian one-concentration approaches was less than prior comparisons (r = 0.823, clinical agreement = 76.8%) and demonstrated the greatest amount of variability with 95% LOA -197 to 153 (MD = -21.9 mg*h/L).ConclusionsLinear and Bayesian two-concentration methods demonstrated high-level agreement with acceptable variability and may be considered comparable to estimate vancomycin AUC24 . As linear and Bayesian one-concentration methods demonstrated significant variability and suboptimal agreement, concerns exist surrounding the interchangeability of these methods in clinical practice, particularly at higher extremes of AUC24 .

Project description:Empiric antimicrobial therapy often consists of the combination of gram-positive coverage with vancomycin (VAN) and gram-negative coverage, specifically an antipseudomonal beta-lactam such as piperacillin-tazobactam (PTZ). Nephrotoxicity is commonly associated with VAN therapy; however, recent reports show higher nephrotoxicity rates among patients treated with the combination of VAN and PTZ.This study evaluated the effect of the VAN/PTZ combination on acute kidney injury (AKI) compared to VAN and PTZ monotherapies.This is a retrospective cohort analysis of adult patients without renal disease receiving VAN, PTZ, or the combination from September 1, 2010 through August 31, 2014 at an academic medical center.The primary outcome was AKI incidence as defined by the Risk, Injury, Failure, Loss, End-stage (RIFLE) criteria.Continuous and categorical variables were assessed with appropriate tests. Univariate and multivariate logistic regressions were performed to assess for associations between variables and AKI incidence. Subanalyses based on severity of illness were performed.Overall, 11,650 patients were analyzed, with 1647 (14.1%) developing AKI. AKI was significantly more frequent in the VAN/PTZ group (21%) compared to either monotherapy group (VAN 8.3%, PTZ 7.8%, P ⟨ 0.001 for both). Combination therapy was independently associated with higher AKI odds compared to monotherapy with either agent (adjusted odds ratio [aOR], 2.03; 95% confidence interval [CI], 1.74-2.39; aOR, 2.31; 95% CI, 1.97-2.71, for VAN and PTZ, respectively). Receipt of concomitant nephrotoxic drugs was independently associated with increased AKI rates, as were increased duration of therapy, hospital length of stay, increasing severity of illness, and increasing baseline renal function.In this study of more than 10,000 patients, VAN combined with PTZ was associated with twice the odds of AKI development compared to either agent as monotherapy. This demonstrates the need for judicious use of combination empiric therapy. Journal of Hospital Medicine 2017;12:77-82.

Dataset Information

Vancomycin Area Under the Curve and Acute Kidney Injury: A Meta-analysis.

Publications

Vancomycin Area Under the Curve and Acute Kidney Injury: A Meta-analysis.

Similar Datasets

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