Evidence map›Paper›PMID 26063050›Full record

ArticleClinical pharmacokinetics2015

Pharmacokinetic Studies in Neonates: The Utility of an Opportunistic Sampling Design.

Stéphanie Leroux, Mark A Turner, Chantal Barin-Le Guellec, Helen Hill, Johannes N van den Anker, Gregory L Kearns, Evelyne Jacqz-Aigrain, Wei Zhao, TINN (Treat Infections in NeoNates) and GRiP (Global Research in Paediatrics) Consortiums

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In one paragraph

Article in Clinical pharmacokinetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed, 1 pooled it
7.0field-weighted citation impact, top 3% 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

45 citing papers in PubMed, 1 synthesis or guideline pooled it, 79 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 7 institutions in 6 countries.

Stéphanie LerouxSino-French Pediatric Research Center, Department of Clinical Pharmacy, School of Pharmaceutical Sciences, Shandong University, 44 West Wenhua Road, 250012, Jinan, China.
Mark A TurnerDepartment of Women's and Children's Health, Institute of Translational Medicine, University of Liverpool, Liverpool, UK.
Chantal Barin-Le GuellecEA4245, Faculté de Médecine, Université François Rabelais, Tours, France.
Helen HillDepartment of Women's and Children's Health, Institute of Translational Medicine, University of Liverpool, Liverpool, UK.
Johannes N van den AnkerIntensive Care, Erasmus MC - Sophia Children's Hospital, Rotterdam, The Netherlands.
Gregory L KearnsDivision of Clinical Pharmacology and Therapeutic Innovation, the Children's Mercy Hospital, Kansas City, MO, USA.
Evelyne Jacqz-AigrainSino-French Pediatric Research Center, Department of Clinical Pharmacy, School of Pharmaceutical Sciences, Shandong University, 44 West Wenhua Road, 250012, Jinan, China.
Wei ZhaoSino-French Pediatric Research Center, Department of Clinical Pharmacy, School of Pharmaceutical Sciences, Shandong University, 44 West Wenhua Road, 250012, Jinan, China. wei.zhao@rdb.aphp.fr.
TINN (Treat Infections in NeoNates) and GRiP (Global Research in Paediatrics) Consortiums
George Washington University · USHôpital Robert-Debré · FRInserm · FRLiverpool Women's Hospital · GBUniversité de Tours · FRUniversity of Liverpool · GBUniversity of Missouri–Kansas City · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectiveThe use of an opportunistic (also called scavenged) sampling strategy in a prospective pharmacokinetic study combined with population pharmacokinetic modelling has been proposed as an alternative strategy to conventional methods for accomplishing pharmacokinetic studies in neonates. However, the reliability of this approach in this particular paediatric population has not been evaluated. The objective of the present study was to evaluate the performance of an opportunistic sampling strategy for a population pharmacokinetic estimation, as well as dose prediction, and compare this strategy with a predetermined pharmacokinetic sampling approach.

methodsThree population pharmacokinetic models were derived for ciprofloxacin from opportunistic blood samples (SC model), predetermined (i.e. scheduled) samples (TR model) and all samples (full model used to previously characterize ciprofloxacin pharmacokinetics), using NONMEM software. The predictive performance of developed models was evaluated in an independent group of patients.

resultsPharmacokinetic data from 60 newborns were obtained with a total of 430 samples available for analysis; 265 collected at predetermined times and 165 that were scavenged from those obtained as part of clinical care. All datasets were fit using a two-compartment model with first-order elimination. The SC model could identify the most significant covariates and provided reasonable estimates of population pharmacokinetic parameters (clearance and steady-state volume of distribution) compared with the TR and full models. Their predictive performances were further confirmed in an external validation by Bayesian estimation, and showed similar results. Monte Carlo simulation based on area under the concentration-time curve from zero to 24 h (AUC24)/minimum inhibitory concentration (MIC) using either the SC or the TR model gave similar dose prediction for ciprofloxacin.

conclusionBlood samples scavenged in the course of caring for neonates can be used to estimate ciprofloxacin pharmacokinetic parameters and therapeutic dose requirements.

Indexed as

Models, BiologicalAdministration, IntravenousAnti-Bacterial AgentsBayes TheoremCiprofloxacinDrug MonitoringFemaleHumansInfantInfant, NewbornMaleMonte Carlo MethodPredictive Value of TestsProspective StudiesReproducibility of ResultsSampling StudiesAnti-Bacterial AgentsCiprofloxacin

Identifiers

PMID26063050
OpenAlexW2184807359

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Registered trials

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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.