A narrative review of predictors for β-lactam antibiotic exposure during empirical treatment in critically ill patients. (3rd April 2021)

• Record Type: Journal Article
• Authors: Abdulla, Alan; Ewoldt, Tim M.J.; Purmer, Ilse M.; Muller, Anouk E.; Gommers, Diederik; Endeman, Henrik; Koch, Birgit C.P.
• Is Part Of: Expert opinion on drug metabolism and toxicology. Volume 17:Number 4(2021)
• Page Start: 359
• Page End: 368
• Publication Date: 2021-04-03
• Subjects: Β-lactam antibiotics -- critically ill -- predictors -- risk factors -- target attainment -- therapeutic drug monitoring; Drugs -- Toxicology -- Periodicals; Drugs -- Metabolism -- Periodicals; 615.7
• DOI: 10.1080/17425255.2021.1879049
• Languages: English
• ISSNs: 1742-5255

Abstract

ABSTRACT: Introduction: : Emerging studies suggest that antibiotic pharmacokinetics (PK) are difficult to predict in critically ill patients. The high intra- and inter-patient PK variability makes it challenging to accurately predict the appropriate dosage required for a given patient. Identifying patients at risk could help clinicians to consider more individualized dosing regimens and perform therapeutic drug monitoring. We provide an overview of relevant predictors associated with target (non-)attainment of β-lactam antibiotics in critically ill patients. Areas covered: : This narrative review summarizes patient and clinical characteristics that can help to predict the attainment of target serum concentrations and to provide guidance on antimicrobial dose optimization. Literature was searched using Embase and Medline database, focusing on β-lactam antibiotics in critically ill patients. Expert opinion: : Adequate concentration attainment can be anticipated in critically ill patients prior to initiating empiric β-lactam antibiotic therapy based on readily available demographic and clinical factors. Male gender, younger age, and augmented renal clearance were the most significant predictors for target non-attainment and should be considered in further investigations to develop dosing algorithms for optimal β-lactam therapy.