Evidence map›Paper›PMID 35662716›Full record

ArticleFrontiers in pharmacology2022

External Evaluation of Population Pharmacokinetic Models to Inform Precision Dosing of Meropenem in Critically Ill Patients.

Nan Yang, Jing Wang, Yueliang Xie, Junjie Ding, Cuifang Wu, Jingjing Liu, Qi Pei

Open access · goldAbstract read
In one paragraph

Article in Frontiers in pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
3.3field-weighted citation impact, top 8% 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

23 citing papers in PubMed, 19 citations in OpenAlex.

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  5. External Evaluation of a Plasma and Cerebrospinal Fluid Population Pharmacokinetics Model of Meropenem in Infants.The journal of pediatric pharmacology and therapeutics : JPPT : the official journal of PPAG · 2026
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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

7 authors at 4 institutions in 2 countries.

Nan YangDepartment of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, China.
Jing WangDepartment of Pharmacy, Xiamen Children's Hospital (Children's Hospital of Fudan University Xiamen Branch), Xiamen, China.
Yueliang XieDepartment of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, China.
Junjie DingCenter for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom.
Cuifang WuDepartment of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, China.
Jingjing LiuDepartment of Intensive Medicine, The Third Xiangya Hospital, Central South University, Changsha, China.
Qi PeiDepartment of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, China.
Central South University · CNThird Xiangya Hospital · CNChildren’s Hospital of Fudan University Xiamen Branch · CNUniversity of Oxford · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Routine clinical meropenem therapeutic drug monitoring data can be applied to model-informed precision dosing. The current study aimed to evaluate the adequacy and predictive capabilities of the published models with routine meropenem data and identify the dosing adaptations using a priori and Bayesian estimation. For this, 14 meropenem models for the external evaluation carried out on an independent cohort of 134 patients with 205 meropenem concentrations were encoded in NONMEM 7.3. The performance was determined using: 1) prediction-based and simulation-based diagnostics; and 2) predicted meropenem concentrations by a priori prediction using patient covariates only; and Bayesian forecasting using previous observations. The clinical implications were assessed according to the required dose adaptations using the meropenem concentrations. All assessments were stratified based on the patients with or without continuous renal replacement therapy. Although none of the models passed all tests, the model by Muro et al. showed the least bias. Bayesian forecasting could improve the predictability over an a priori approach, with a relative bias of -11.63-68.89% and -302.96%-130.37%, and a relative root mean squared error of 34.99-110.11% and 14.78-241.81%, respectively. A dosing change was required in 40.00-68.97% of the meropenem observation results after Bayesian forecasting. In summary, the published models couldn't adequately describe the meropenem pharmacokinetics of our center. Although the selection of an initial meropenem dose with a priori prediction is challenging, the further model-based analysis combining therapeutic drug monitoring could be utilized in the clinical practice of meropenem therapy.

Indexed as

Bayesian forecastingcritically ill patientsexternal evaluationmeropenempopulation pharmacokinetics

Identifiers

PMID35662716
PMCPMC9157771
OpenAlexW4280507050

What OpenQuestion holds

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LicenceCC BY
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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.