Evidence map›Paper›PMID 42272833›Full record

ArticleFrontiers in pharmacology2026

Machine-learning prediction of pre-dose pharmacokinetics optimizes initial vancomycin dosing in critically ill children.

Jihui Chen, Libo Dai, Haixin Xu, Yunlu Yao, Xiaohui Huang, Jing Ma, Xinzhu Liu, Huijuan Yao, Jiru Li, Dan Wu and 5 more

Abstract read
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In one paragraph

Article in Frontiers in pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

Jihui Chen *Department of Clinical Pharmacy, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Libo Dai *Department of Clinical Pharmacy, Inner Mongolia Autonomous Region People's Hospital, Hohhot, China.
Haixin Xu *Department of Clinical Pharmacy, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Yunlu YaoDepartment of Clinical Pharmacy, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Xiaohui HuangDepartment of Clinical Pharmacy, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Jing MaDepartment of Clinical Pharmacy, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Xinzhu LiuDepartment of Clinical Pharmacy, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Huijuan YaoDepartment of Clinical Pharmacy, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Jiru LiDepartment of Pediatric Critical Care Medicine, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Dan WuDepartment of Clinical Pharmacy, Inner Mongolia Autonomous Region People's Hospital, Hohhot, China.
Jincheng SunDepartment of Clinical Pharmacy, Inner Mongolia Autonomous Region People's Hospital, Hohhot, China.
Ze YuBeijing Medicinovo Technology Co. Ltd., Beijing, China.
Yanhui LiInner Mongolia Research Institute, Shanghai Jiao Tong University, Hohhot, China.
Hongxin YangDepartment of Clinical Pharmacy, Inner Mongolia Autonomous Region People's Hospital, Hohhot, China.
Shuhong BuDepartment of Clinical Pharmacy, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To develop and validate a machine-learning/population-pharmacokinetic (ML-PPK) hybrid model that predicts individual vancomycin clearance (CL) and volume of distribution (V Patients and methods: We retrospectively analysed children from two tertiary centers in China (2013-2023). A previously published one-compartment PPK model was re-estimated with the pooled dataset and used as a Bayesian prior to derive individual CL and V Results: Data from 821 children and 1,767 vancomycin concentrations were included. 29 candidate variables were screened, and six high-impact predictors - body weight, cardiothoracic surgery, estimated glomerular filtration rate, sex, ICU admission, and post-menstrual-age class - maximized performance. CatBoost outperformed the other evaluated algorithms and, under this study design, more closely approximated PPK-Bayesian posterior PK estimates than the original parametric PPK covariate model, yielding for CL: R Conclusion: An interpretable CatBoost-based ML-PPK hybrid can estimate CL and V

Indexed as

initial dosingmachine learningmodel-informed precision dosingpopulation pharmacokineticsvancomycin

Identifiers

PMID42272833
PMCPMC13246369

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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.