Clinical trial NCT03954275: Detailed Assessment of Augmented Renal Clearance in a Large Mixed Intensive Care Unit Population
Official title: Detailed Assessment of Augmented Renal Clearance in a Large Mixed Intensive Care Unit Population
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Critical Illness
Augmented Renal Clearance
Lead sponsor: Universitaire Ziekenhuizen KU Leuven
Sponsor class: OTHER
Collaborators: name: University Hospital, Ghent
name: University Hospital, Antwerp
First posted: 2019-05-17
Enrollment: 10000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Patients admitted at any intensive care unit (surgical, medical, cardiac) from the 3 participating centra (Leuven University Hospitals, Ghent University Hospital, Antwerp University Hospital) between 2013 and present

Brief 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.

Detailed 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.

Eligibility criteria: Inclusion Criteria:

* Having at least one 24h creatinine clearance measurement available

Exclusion Criteria:

* Any form of renal replacement therapy

Primary outcomes:
measure: ARC incidence per day
timeFrame: Retrospective analysis between January 2013 and December 2015
measure: ARC incidence per admission
timeFrame: Retrospective analysis between January 2013 and December 2015
measure: Duration and course of ARC episodes
timeFrame: Retrospective analysis between January 2013 and December 2015
measure: ARC daily prevalence
timeFrame: Retrospective analysis between January 2013 and December 2015
measure: Logistic regression with ARC as dependent variable
timeFrame: Retrospective analysis between January 2013 and December 2015
measure: Predictive algorithm for ARC
timeFrame: Retrospective analysis between January 2013 and December 2015
measure: Most precise formula using Bland-Altman agreement analysis
timeFrame: Retrospective analysis between January 2013 and December 2015
measure: Performance of the best cutoff for ARC using ROC curve analysis
timeFrame: Retrospective analysis between January 2013 and December 2015
measure: Exploration of clinical impact of ARC via surrogate markers
timeFrame: Retrospective analysis between January 2013 and December 2015
Secondary outcomes:
Other outcomes:
Locations:
city: Leuven
country: Belgium
facility: UZLeuven
Study officials:
affiliation: Universitaire Ziekenhuizen KU Leuven
name: Isabel Spriet, PhD
role: STUDY_DIRECTOR
References:
citation: Gijsen M, Huang CY, Flechet M, Van Daele R, Declercq P, Debaveye Y, Meersseman P, Meyfroidt G, Wauters J, Spriet I. Development and External Validation of an Online Clinical Prediction Model for Augmented Renal Clearance in Adult Mixed Critically Ill Patients: The Augmented Renal Clearance Predictor. Crit Care Med. 2020 Dec;48(12):e1260-e1268. doi: 10.1097/CCM.0000000000004667.