Pharmacokinetic Evaluation of Cefepime Administrered Intravenously in Intensive Care Patients
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 20
- 主要终点
- Median absolute predictive error (MdAPE) of population PK model without covariates
研究概览
简要总结
Several population pharmacokinetic (PK) models for cefepime in critically ill patients have been described, all indicating that variability in renal clearance is the main determinant of observed variability in exposure. The main objective of this study was hence to determine which renal marker best predicts cefepime clearance.
详细描述
Timely and appropriate antibiotic therapy, sufficient to guarantee adequate antibiotic concentrations in blood and tissues, is one of the most important interventions in critically ill patients with infections.1,2 Cefepime is a fourth generation cephalosporin with broad spectrum activity against Gram-negative bacteria that is used as empirical and directed therapy for severe infections like sepsis and pneumonia. Nevertheless, administration of adequate antibiotic doses is a real challenge in critically ill patients because the pharmacokinetics (PK) of these drugs may be influenced by the complex pathophysiological changes that occur during sepsis.2 Recent reviews described the enormous pharmacokinetic variability of beta-lactam antibiotics in critically ill patients.3,4 Therefore, strategies for dose individualization are explored in an attempt to better control a patient's exposure to the antibiotic, thereby potentially improving the prognosis of critically ill patients with infection. On the one hand, several smaller studies have already shown that better outcomes for critically ill patients can be expected with higher drug exposures, at least for less severely ill patients.5,6 This conclusion was supported by the DALI study, a large multi-center prospective study.7 On the other hand, it was shown that insufficient antibiotic exposure may lead to the development of antibiotic resistance.8 This link was initially shown with inappropriately low quinolone exposures, but more recently with other classes of antibiotics including beta-lactams.9,10 In addition to ensuring that plasma levels are high enough for optimal antimicrobial activity and suppressing emergence of resistance, individualized dosing might offer a perspective to prevent potential side-effects originating from toxic plasma levels. This seems particularly relevant for cefepime, a beta-lactam antibiotic, as it was shown that cefepime is an underappreciated cause of neurotoxicity, especially in intensive care unit (ICU) patients,11,12 patients with impaired renal function,13-16 and patients with brain disorders.17 Population pharmacokinetic models provide a quantitative view of the effect of particular individual factors on the plasma concentration time profile of a drug. Population PK models thereby help to establish individual treatment regimen in patients, depending on the specific patient covariates that were included in the model. As cefepime is a hydrophilic compound, drug elimination is mainly determined by renal clearance and to a lesser extent by non-renal clearance. Therefore, renal markers have been explored as the main determinant to predict cefepime variability in population PK models.18-24 However, none of the published PK models was developed using both plasma and urinary data, though having access to both matrices may be an advantage to identify clinically relevant covariates. Moreover, only creatinine-based markers were used as covariates and, up to now, it was unclear whether the newer markers to assess renal function (e.g. cystatine C, uromodulin and Kidney Injury Moleclure-1 (KIM-1)) are more accurate to predict cefepime clearance.
In this study, a clinical trial was conducted to develop a population PK model for cefepime in critically ill patients assessing renal and non-renal clearance separately, based on both plasma and urinary cefepime concentrations. This model then served as a tool to compare the adequacy of six different renal markers as predictors for renal cefepime clearance. After integrating the most adequate predictor into the PK model, the final model was used to evaluate current dose recommendations for cefepime.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Treatment
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —(Adult, Older Adult)
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patient age 18 years or more
- •Hospitalized in the ICU of OLV hospital Aalst
- •Elected by the treating physician to receive cefepime,irrespectively of the study
- •Presence of arterial or central line for blood sampling
排除标准
- •Exact time of cefepime administration or blood sampling unknown
- •No written informed consent by the patient or his/her (legal) representative
研究组 & 干预措施
Study arm
- Cefepime dosing
- Blood sampling
- Urine sampling
- Determination of renal markers
- Population pharmacokinetic modeling
- Covariate screening
- Monte Carlo simulations
干预措施: Cefepime dosing (Drug)
Study arm
- Cefepime dosing
- Blood sampling
- Urine sampling
- Determination of renal markers
- Population pharmacokinetic modeling
- Covariate screening
- Monte Carlo simulations
干预措施: Blood sampling (Other)
Study arm
- Cefepime dosing
- Blood sampling
- Urine sampling
- Determination of renal markers
- Population pharmacokinetic modeling
- Covariate screening
- Monte Carlo simulations
干预措施: Urine sampling (Other)
Study arm
- Cefepime dosing
- Blood sampling
- Urine sampling
- Determination of renal markers
- Population pharmacokinetic modeling
- Covariate screening
- Monte Carlo simulations
干预措施: Determination of renal markers (Other)
Study arm
- Cefepime dosing
- Blood sampling
- Urine sampling
- Determination of renal markers
- Population pharmacokinetic modeling
- Covariate screening
- Monte Carlo simulations
干预措施: Population pharmacokinetic modeling (Other)
Study arm
- Cefepime dosing
- Blood sampling
- Urine sampling
- Determination of renal markers
- Population pharmacokinetic modeling
- Covariate screening
- Monte Carlo simulations
干预措施: Covariate screening (Other)
Study arm
- Cefepime dosing
- Blood sampling
- Urine sampling
- Determination of renal markers
- Population pharmacokinetic modeling
- Covariate screening
- Monte Carlo simulations
干预措施: Monte Carlo simulations (Other)
结局指标
主要结局
Median absolute predictive error (MdAPE) of population PK model without covariates
时间窗: Evaluation during a maximum follow-up period of 5 days
Median absolute predictive error (MdAPE) of population PK model with different renal markers incorporated
时间窗: Evaluation during a maximum follow-up period of 5 days
次要结局
- The estimated probability of target attianment (%) for the different categories of the Sanford guide(Based on data from a maximum follow-up period of 5 days)
- The estimated probability of toxic levels (%) for the different categories of the Sanford guide(Based on data from a maximum follow-up period of 5 days)
研究者
Stijn Jonckheere
Principal Investigator
Onze Lieve Vrouw Hospital
