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

Critical Illness Clinical Trial

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT03954275
• Other study ID #: S61364
• Status: Recruiting
• Start date: October 1, 2018
• Est. completion date: September 2019

Study information

• Verified date: May 2019
• Source: Universitaire Ziekenhuizen Leuven
• Contact: Matthias Gijsen, PharmD
• Phone: 16 340087
• Email: matthias.gijsen[@]uzleuven.be
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

Description:

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 10000
• Est. completion date: September 2019
• Est. primary completion date: August 2019
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Having at least one 24h creatinine clearance measurement available

Exclusion Criteria:

• Any form of renal replacement therapy

Related Conditions & MeSH terms

• Augmented Renal Clearance
• Critical Illness

Intervention

Other:
• no intervention
no intervention

Locations

Country	Name	City	State
Belgium	UZLeuven	Leuven

Sponsors (3)

Lead Sponsor	Collaborator
Universitaire Ziekenhuizen Leuven	University Hospital, Antwerp, University Hospital, Ghent

Country where clinical trial is conducted

Belgium, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	ARC incidence per day	Incidence of ARC per 100 ICU days	Retrospective analysis between January 2013 and December 2015
Primary	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
Primary	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
Primary	ARC daily prevalence	Daily prevalence of ARC (% of ARC days per ICU admission day)	Retrospective analysis between January 2013 and December 2015
Primary	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
Primary	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
Primary	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
Primary	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
Primary	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