接受β-内酰胺类抗菌药物治疗的重症成人群体药代动力学研究中的变量:系统综述和叙述性综述。

IF 3.7 Q2 INFECTIOUS DISEASES
JAC-Antimicrobial Resistance Pub Date : 2024-02-26 eCollection Date: 2024-02-01 DOI:10.1093/jacamr/dlae030
Jan Hansel, Fahmida Mannan, Rebecca Robey, Mary Kumarendran, Siân Bladon, Alexander G Mathioudakis, Kayode Ogungbenro, Paul Dark, Timothy W Felton
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引用次数: 0

摘要

导言:对重症患者进行的 β-内酰胺类抗菌药物群体药代动力学研究得出的模型可为用药提供依据。在非线性混合效应模型中,常用协变量来改善模型拟合度和解释变异性。我们的目的是调查哪些协变量最常被评估,哪些协变量被认为是重要的,以及全球的发表模式:我们进行了一项系统性综述,检索了 2023 年 3 月 1 日的 MEDLINE、Embase、CENTRAL 和 Web of Science,包括对接受 β-内酰胺类抗菌药物治疗的成人重症患者进行的研究,这些患者在群体药代动力学研究中接受了血液采样。我们对所有报告的协变量进行了提取和分类,并使用 ClinPK 检查表对报告质量进行了评估:我们的搜索发现了 151 项研究,共有 6018 名参与者。大多数研究报告了观察性队列(120 项研究,占 80%),大多数研究在高收入环境中进行(136 项研究,占 90%)。在确定的 1083 个协变量中,237 个是唯一的;最常见的类别是患者特征(404 个)、生物标志物(206 个)和生理参数(163 个)。只有 7 个不同的常见协变量(CLCR、体重、肾小球滤过率、利尿、肾脏替代需求、血清白蛋白和 C 反应蛋白)在超过 20% 的情况下具有显著性:协变量的选择最常见的依据是生物学合理性,其中患者特征和生物标志物是最常被研究的。我们开发了一个可公开访问的协变量数据库,以帮助研究人员在设计群体药代动力学研究时选择协变量。脓毒症亚型等新的协变量尚未被研究,这为今后的工作留下了研究空白。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Covariates in population pharmacokinetic studies of critically ill adults receiving β-lactam antimicrobials: a systematic review and narrative synthesis.

Introduction: Population pharmacokinetic studies of β-lactam antimicrobials in critically ill patients derive models that inform their dosing. In non-linear mixed-effects modelling, covariates are often used to improve model fit and explain variability. We aimed to investigate which covariates are most commonly assessed and which are found to be significant, along with global patterns of publication.

Methods: We conducted a systematic review, searching MEDLINE, Embase, CENTRAL and Web of Science on 01 March 2023, including studies of critically ill adults receiving β-lactam antimicrobials who underwent blood sampling for population pharmacokinetic studies. We extracted and categorized all reported covariates and assessed reporting quality using the ClinPK checklist.

Results: Our search identified 151 studies with 6018 participants. Most studies reported observational cohorts (120 studies, 80%), with the majority conducted in high-income settings (136 studies, 90%). Of the 1083 identified covariate instances, 237 were unique; the most common categories were patient characteristics (n = 404), biomarkers (n = 206) and physiological parameters (n = 163). Only seven distinct commonly reported covariates (CLCR, weight, glomerular filtration rate, diuresis, need for renal replacement, serum albumin and C-reactive protein) were significant more than 20% of the time.

Conclusions: Covariates are most commonly chosen based on biological plausibility, with patient characteristics and biomarkers the most frequently investigated. We developed an openly accessible database of reported covariates to aid investigators with covariate selection when designing population pharmacokinetic studies. Novel covariates, such as sepsis subphenotypes, have not been explored yet, leaving a research gap for future work.

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CiteScore
5.30
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