Detailed Assessment of Augmented Renal Clearance in a Large Mixed Intensive Care Unit Population

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

Official Title
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Detailed Assessment of Augmented Renal Clearance in a Large Mixed Intensive Care Unit Population

Conditions
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Critical Illness, Augmented Renal Clearance

Intervention / Treatment
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* Other: no intervention


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Having at least one 24h creatinine clearance measurement available Exclusion Criteria: Any form of renal replacement therapy

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Critically ill patients with a CrCl24h<br>Patients admitted to any intensive care unit (surgical, medical or cardiac) and having at least one 24h creatinine clearance measurement available. | Other: no intervention<br>* no intervention<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| ARC incidence per day | Incidence of ARC per 100 ICU days | Retrospective analysis between January 2013 and December 2015 | 
| ARC incidence per admission | 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) | Retrospective analysis between January 2013 and December 2015 | 
| Duration and course of ARC episodes | 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) | Retrospective analysis between January 2013 and December 2015 | 
| ARC daily prevalence | Daily prevalence of ARC (% of ARC days per ICU admission day) | Retrospective analysis between January 2013 and December 2015 | 
| Logistic regression with ARC as dependent variable | Risk factors associated with ARC will be identified through logistic regression analysis on demographic and clinical data. | Retrospective analysis between January 2013 and December 2015 | 
| Predictive algorithm for ARC | 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. | Retrospective analysis between January 2013 and December 2015 | 
| Most precise formula using Bland-Altman agreement analysis | 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). | Retrospective analysis between January 2013 and December 2015 | 
| Performance of the best cutoff for ARC using ROC curve analysis | Performance of the best cutoff for ARC using ROC curve analysis on the most precise formula estimating renal function. | Retrospective analysis between January 2013 and December 2015 | 
| Exploration of clinical impact of ARC via surrogate markers | Vancomycin and aminoglycoside (amikacin & gentamycin) serum concentrations will be used as surrogate markers to evaluate potential clinical impact of ARC. | Retrospective analysis between January 2013 and December 2015 |