Recommended Antibiotic Dosage Regimens in Critically Ill Patients with Augmented Renal Clearance: A Systematic Review

加药 医学 抗生素 病危 药代动力学 药效学 重症监护医学 人口 肾功能 药理学 内科学 生物 微生物学 环境卫生
作者
Catarina Mendes Silva,João Pedro Baptista,Iolanda Santos,Paulo Martins
出处
期刊:International Journal of Antimicrobial Agents [Elsevier BV]
卷期号:59 (5): 106569-106569 被引量:21
标识
DOI:10.1016/j.ijantimicag.2022.106569
摘要

Augmented renal clearance (ARC) is common in critically ill patients and may lead to subtherapeutic levels of antibiotics, thus influencing clinical outcomes and emergence of multidrug-resistant bacteria. The aim of this systematic review was to search the literature for recommendations concerning dosage adjustment for antibiotics administered to critically ill patients with ARC.A search of three electronic databases (Pubmed, Embase and Cochrane) was conducted from inception until the end of March 2021, using terms related to: 1) pharmacokinetics/pharmacodynamics (PK/PD), 2) antibiotic, 3) ARC and 4) critically ill. Two reviewers searched for relevant data and included studies suggesting specific doses for critically ill patients with ARC.Forty-seven studies met the inclusion criteria. Dosage recommendations were found for 18 antibiotics. Differences were found in population characteristics, ARC definition, creatinine clearance (CLCR) determination method, PK methodology and definition of PK/PD targets. Cut-off values for CLCR ranged 120-240 mL/min; the most frequently employed method to define CLCR was Cockcroft-Gault estimation; and 83% of studies used population PK models to predict dosing regimens. All antibiotics, except three, needed upward dosing and/or infusion modality adjustments to reach PK/PD targets.Despite the lack of high-quality studies and high heterogeneity, incremental dosing adjustment of antibiotics was frequently needed for critically ill patients with ARC to achieve the desired PK/PD targets. More research is needed to enlarge the number of antibiotics with recommendations for ARC and to validate current suggestions based on mathematical models in a clinical scenario.
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