ArticlePharmacotherapy2026
Multisite Analysis of Vancomycin Concentration Predictions in a Bayesian Software in Adult and Pediatric Patients With and Without Acute Kidney Injury.
Article in Pharmacotherapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBayesian software programs are recommended for area under the concentration-time curve (AUC)-guided vancomycin monitoring, but hesitancy remains for patients with acute kidney injury (AKI). Here, we assessed agreement between population pharmacokinetic model-based Bayesian predictions and observed vancomycin concentrations in patients with and without AKI.
methodsData were retrospectively analyzed from patients at 162 institutions in the United States. Courses with ≥ 2 vancomycin doses, ≥ 1 vancomycin level, and ≥ 2 serum creatinine values were included. Observed drug levels served as reference values and were classified as occurring in the absence of AKI, during new-onset AKI, or during persistent AKI. Adult, pediatric, and neonate pharmacokinetic models were assessed both a priori and a posteriori. Performance was measured using root mean squared error, mean percentage error, and the proportion of predictions within ±20% (P20) and ±30% (P30) of observed values. We also evaluated flattened priors and last-observation-only weighting as alternative Bayesian estimation approaches and reported agreement between model-predicted and model-estimated AUCs (classified as in or out of target).
resultsThere were 30,561 adults, 5223 pediatrics, and 1865 neonates included. Prediction performance declined with AKI relative to no AKI, particularly in pediatrics, followed by neonates, with adults showing minimal deterioration. Flattened priors often improved performance, whereas last-observation-only typically worsened it. Overall AKI reduced P20 by 0%-33% and P30 by 0%-19% (absolute percentage points). AUC classification agreement was similar across no AKI, new-onset AKI, and persistent AKI groups (73%-83%, 72%-82%, and 72%-84%, respectively, a posteriori).
conclusionModel predictiveness of vancomycin concentrations was worse in patients with AKI, especially in pediatrics and neonates, and generally less pronounced in persistent versus new-onset AKI. Further studies with denser pharmacokinetic sampling and elucidation of clinical implications of Bayesian-guided vancomycin dosing in AKI are needed.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.