Evidence map›Paper›PMID 35640658›Full record

ArticleThe Journal of antimicrobial chemotherapy2022

Does calculation method matter for targeting vancomycin area under the curve?

Jack Chang, Dhara Patel, Ana Vega, Kimberly C Claeys, Emily L Heil, Marc H Scheetz

Open access · greenAbstract read
In one paragraph

Article in The Journal of antimicrobial chemotherapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 45% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 1 country.

Jack ChangMidwestern University College of Pharmacy, Department of Pharmacy Practice, Downers Grove, IL, USA.
Dhara PatelMidwestern University College of Pharmacy, Department of Pharmacy Practice, Downers Grove, IL, USA.
Ana VegaJackson Memorial Hospital, Department of Pharmacy, Miami, FL, USA.
Kimberly C ClaeysUniversity of Maryland School of Pharmacy, Department of Pharmacy Practice and Science, Baltimore, MD, USA.
Emily L HeilUniversity of Maryland School of Pharmacy, Department of Pharmacy Practice and Science, Baltimore, MD, USA.
Marc H ScheetzMidwestern University College of Pharmacy, Department of Pharmacy Practice, Downers Grove, IL, USA.ORCID 0000-0002-1091-6130
Midwestern University · USUniversity of Maryland, Baltimore · USJackson Memorial Hospital · US

Funding

Quantifying renal injury among the most commonly used clinical antibiotic combinationsR21AI149026 · NIAID · MIDWESTERN UNIVERSITY · PI SCHEETZ, MARC HOWARD · 2020 to 2021
$413k
NIAID NIH HHS R21 AI149026
6 · The paper itself

Abstract

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.

Indexed as

Anti-Bacterial AgentsVancomycinAdultArea Under CurveBayes TheoremDrug MonitoringHumansAnti-Bacterial AgentsVancomycin

Identifiers

PMID35640658
PMCPMC9890897
OpenAlexW4225698749

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.