ArticlePharmacotherapy2026
Bayesian Dosing Simulator (BDS): A Pharmacokinetic Modeling Tool for Optimized Antibiotic Therapy.
Article in Pharmacotherapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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.
- From Intelligent Operating Room to Smart ICU: Digital Continuity, AI-Supported Decision-Making and CRRT as a Model of Pharmacokinetic Personalization in Critical Care.Healthcare (Basel, Switzerland) · 2026Review
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
Abstract
backgroundMeropenem is widely used to treat hospital-acquired pneumonia in critically ill patients, with efficacy dependent on the time that free concentrations exceed the minimum inhibitory concentration (fT > MIC). In practice, single-sample therapeutic drug monitoring (TDM) may not ensure target attainment, particularly in patients requiring continuous renal replacement (CRRT). Model-informed precision dosing (MIPD) enables individualized, real-time adjustments, but implementation is limited. Herein, we developed a Shiny application for Bayesian meropenem dose optimization.
methodsA previously published nonparametric model was translated into a parametric Bayesian framework with maximum a posteriori (MAP) updating implemented through a Shiny interface. Plasma pharmacokinetic (PK) data from critically ill patients served for external validation. Posterior predictions were benchmarked against population and individual (MAP) outputs from Pmetrics 3.09 and population median profiles from 1000 Monte Carlo simulations per patient. Predictive performance was assessed using relative median prediction error (rMPE) and relative median absolute prediction error (rMAPE), for the combined cohort overall and stratified by CRRT status. Sparse sampling scenarios (one, two, and three observations per patient) were also evaluated.
resultsEighteen patients were evaluated (non-CRRT n = 13; CRRT n = 5). In Pmetrics, rMPE ranged from -7.7% to 9.4% and rMAPE from 18.5% to 25.5% across renal replacement strata and prediction types. Simulation-based population predictions yielded greater error, with rMPE/rMAPE of -25%/34% in CRRT patients and -21%/30% in non-CRRT patients. Across Bayesian Dosing Simulator (BDS) sampling scenarios, prediction error remained within prespecified limits (rMPE ±20%, rMAPE ≤ 30%), with F20 ranging from 80% to 94% and F30 from 87% to 94%.
conclusionA validated nonparametric meropenem model was successfully implemented within an open-source Bayesian framework yielding predictive accuracy comparable to the reference model with robust performance under sparse sampling, supporting its feasibility for individualized meropenem dosing. Prospective evaluation of its clinical safety and effectiveness is 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.