Detailed Assessment of Augmented Renal Clearance in a Large Mixed Intensive Care Unit Population
试验速览
- 阶段
- 不适用
- 入组人数
- 10,000
- 试验地点
- 1
- 主要终点
- Most precise formula using Bland-Altman agreement analysis
研究概览
简要总结
This multi-center retrospective cohort study presents a detailed assessment of augmented renal clearance (ARC) in a mixed population of adult critically ill patients. Epidemiology of ARC will be studied in detail in a very heterogeneous population. Risk factors for ARC will be identified and a predictive scoring system for ARC ready to use in clinical practice will be constructed and validated. Performance of estimators of kidney function will be measured and a cutoff for ARC will be determined for the best estimator. Finally clinical impact of ARC will be explored using vancomycine and aminoglycosides levels as surrogate marker.
详细描述
Augmented renal clearance will be assessed in detail in a very large and heterogeneous adult critically ill population. Analysis will be conducted retrospectively on a multi-center database collected by the M@tric research group. M@tric collects data from all intensive care units (surgical, medical, cardiac) in 3 Belgian University Hospitals (Leuven, Ghent, Antwerp).
Anonymised admission, demographic, clinical and laboratory data collected from 2013 until the present will be retrieved from the M@tric database. These data will then be coded and analysed in R statistical software. ARC will be defined based on a 24h creatinine clearance (CrCl24h) >=130ml/min/1.73m².
Epidemiology and risk factors for ARC will be studied in order to confirm and clarify past studies which have mostly been done in rather small and specific subsets of patients. A predictive algorithm for ARC will be trained and subsequently validated for use in clinical practice. Moreover this algorithm will be compared to existing scoring systems, which have not yet found their way into clinical practice. This algorithm will provide the ability to anticipate ARC on the intensive care unit. Also use of formulae estimating renal function will be evaluated in this population. These estimators will be compared to the CrCl24h, which is considered the golden standard in clinical practice. A cutoff for the best estimating formula in order to detect ARC will be calculated. Finally the impact of ARC on serum levels of hydrophilic molecules likes vancomycine and aminoglycosides will be studied. As this research follows a retrospective design these levels will be used a surrogate marker for clinical impact. This will potentially point out some opportunities for future research on the clinical impact of ARC.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
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
- •Having at least one 24h creatinine clearance measurement available
排除标准
- •Any form of renal replacement therapy
结局指标
主要结局
Most precise formula using Bland-Altman agreement analysis
时间窗: Retrospective analysis between January 2013 and December 2015
Bland-Altman agreement analysis between CrCl24h and 3 commonly used serum creatinine based formulae estimating renal function (CKD-EPI, C\&G, MDRD) will be used to identify the formula with the best precision (SD of the bias).
ARC daily prevalence
时间窗: Retrospective analysis between January 2013 and December 2015
Daily prevalence of ARC (% of ARC days per ICU admission day)
Performance of the best cutoff for ARC using ROC curve analysis
时间窗: Retrospective analysis between January 2013 and December 2015
Performance of the best cutoff for ARC using ROC curve analysis on the most precise formula estimating renal function.
ARC incidence per admission
时间窗: Retrospective analysis between January 2013 and December 2015
Incidence of ARC in % of ICU admissions: with ARC incidence defined as at least once, min. 50% of the measurements, 100% of the measurements during ICU admission)
Logistic regression with ARC as dependent variable
时间窗: Retrospective analysis between January 2013 and December 2015
Risk factors associated with ARC will be identified through logistic regression analysis on demographic and clinical data.
Predictive algorithm for ARC
时间窗: Retrospective analysis between January 2013 and December 2015
An algorithm predicting ARC on the next day(s) will be created using a backward selection logistic regression model on the risk factors associated with ARC detected in this study and/or in previously published studies.
Exploration of clinical impact of ARC via surrogate markers
时间窗: Retrospective analysis between January 2013 and December 2015
Vancomycin and aminoglycoside (amikacin \& gentamycin) serum concentrations will be used as surrogate markers to evaluate potential clinical impact of ARC.
ARC incidence per day
时间窗: Retrospective analysis between January 2013 and December 2015
Incidence of ARC per 100 ICU days
Duration and course of ARC episodes
时间窗: Retrospective analysis between January 2013 and December 2015
ARC episodes: number of episodes (count), length of the episodes (days) and both combined to obtain relative contribution to ARC as a % ((count\*length)/total ARC days)
次要结局
未报告次要终点
研究者
Matthias Gijsen
Principal investigator
Universitaire Ziekenhuizen KU Leuven