Evidence map›Paper›PMID 34342716›Full record

ArticleEuropean journal of clinical pharmacology2021

External evaluation of published population pharmacokinetic models of polymyxin B.

Ya-Qian Li, Kai-Feng Chen, Jun-Jie Ding, Hong-Yi Tan, Nan Yang, Ya-Qi Lin, Cui-Fang Wu, Yue-Liang Xie, Guo-Ping Yang, Jing-Jing Liu and 1 more

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Article in European journal of clinical pharmacology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

11 authors.

Ya-Qian LiDepartment of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China.
Kai-Feng ChenDepartment of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China.
Jun-Jie DingCenter for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK.
Hong-Yi TanCenter for Clinical Pharmacology, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China.
Nan YangDepartment of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China.
Ya-Qi LinDepartment of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China.
Cui-Fang WuDepartment of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China.
Yue-Liang XieDepartment of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China.
Guo-Ping YangCenter for Clinical Pharmacology, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China.
Jing-Jing LiuDepartment of Intensive Medicine, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China. Liujj@126.com.
Qi PeiDepartment of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China. peiqi1028@126.com.ORCID http://orcid.org/0000-0002-6711-4251

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesSeveral population pharmacokinetics (popPK) models for polymyxin B have been constructed to optimize therapeutic regimens. However, their predictive performance remains unclear when extrapolated to different clinical centers. Therefore, this study aimed to evaluate the predictive ability of polymyxin B popPK models.

methodsA literature search was conducted, and the predictive performance was determined for each selected model using an independent dataset of 20 patients (92 concentrations) from the Third Xiangya Hospital. Prediction- and simulation-based diagnostics were used to evaluate model predictability. The influence of prior information was assessed using Bayesian forecasting.

resultsEight published studies were evaluated. In prediction-based diagnostics, the prediction error within ± 30% was over 50% in two models. In simulation-based diagnostics, the prediction- and variability-corrected visual predictive check (pvcVPC) showed satisfactory predictivity in three models, while the normalized prediction distribution error (NPDE) tests indicated model misspecification in all models. Bayesian forecasting demonstrated a substantially improvement in the model predictability even with one prior observation.

conclusionNot all published models were satisfactory in prediction- and simulation-based diagnostics; however, Bayesian forecasting improved the predictability considerably with priors, which can be applied to guide polymyxin B dosing recommendations and adjustments for clinicians.

Indexed as

Models, BiologicalBayes TheoremHumansImmunosuppressive AgentsPolymyxin BImmunosuppressive AgentsPolymyxin BExternal evaluationPolymyxin BPopulation pharmacokinetics

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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.