NCT01981993

Validation of a Urinary Biomarker as Diagnostic Tool for AKI in Sepsis

Completed Last updated 3 May 2023

What this trial tests

trial testing Urinary proteomic analysis in Sepsis at Intensive Care Unit in 150 participants. Completed in 27 March 2011.

Timeline

4 June 2009

Primary endpoint
27 March 2011

27 March 2011

Quick facts

Lead sponsor	University Hospital, Ghent
Status	Completed
Study type	OBSERVATIONAL
Enrollment	150
Start date	4 June 2009
Primary completion	27 March 2011
Estimated completion	27 March 2011
Sites	1 location across Belgium

Drugs / interventions tested

• Urinary proteomic analysis — full drug profile →

Conditions studied

• Sepsis at Intensive Care Unit — all drugs for Sepsis at Intensive Care Unit →

Sponsor

University Hospital, Ghent

Who can join

18 and older, any sex, with Sepsis at Intensive Care Unit. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

BACKGROUND:

Early diagnosis and prognostication of acute kidney injury in patients with sepsis is key to further our understanding this disease and in the evaluation of new interventions for this condition. Many urinary biomarkers have been proposed, but no single one seems to consistently provide additional information on top of clinical and routine biochemical parameters. Some authors have proposed to use a panel of urinary biomarkers to increase the accuracy However, this approach has so far not been tested in a large group of patients with sepsis. In addition, newer and more performant analytical techniques have been developed that warrant testing in the clinical field.

PATIENTS AND METHODS:

At least 150 consecutive patients admitted to a tertiary care intensive care unit (ICU) with sepsis will be included. After bladder catheterisation, urinary samples will be collected at time points 0, 4 hours and 24 hours after admission, and further daily on day 1-5. Samples will be immediately centrifuged and frozen at -80°C until analysis. Samples will be extracted by removing larger proteins (\>20kDa) and de-salting step prior to mass spectrometry analysis. Investigators will use capillary electrophoresis-mass spectrometry (CE-MS) to assess urinary peptides predictive of AKI: 20 peptides constituting the AKI marker pattern previously established from a cohort of ICU patients. Simultaneously, samples will be analysed using matrix-assisted laser desorption ionisation time-of-flight mass spectrometry (MALDI-TOF MS), an alternative platform to CE-MS, which is currently being developed for routine ICU use. A proof of concept of the technique involved has been successfully applied to a set of urine samples from patients diagnosed with diabetes presenting normoalbuminuria (controls) and macroalbuminuria (cases).

Clinical, demographic and biochemical data of patients will be collected during the first 5 days.

PATIENT OUTCOME

* in the short term:

* development of acute kidney injury according to RIFLE criteria
* death
* need for renal replacement therapy during ICU stay
* on the longer term

* death
* need for renal replacement therapy
* estimated glomerular filtration rate as calculated by MDRD at 3 months, 1 year and 2 years.

Using cut-offs , Receiver Operating Characteristics curves, negative and positive predictive value will be used to describe diagnostic performance of the biomarker panel alone, or in combination with basic clinical and/or routine biochemical parameters. Univariate and multivariate logistic regression for death will be used to evaluate prognostication value of the biomarker set.

In addition, new discriminatory cut-offs of proteomic patterns as determined by more recent proteomic analysis techniques will be determined in a training set (half of the cohort) and validated in the other half of the cohort. Using the MALDI-TOF MS platform, investigators will assess urinary peptides that were predictive of AKI in a training set (ca. 75 patients) with good diagnostic performance of the marker panel (accuracy above 0.8) . Performance of the biomarker panel will be assessed in a blinded test set of ca. 75 patients to evaluate validity of the model in AKI detection.

Publications & conference data

3 peer-reviewed publications reference this trial (live from Europe PMC):

1. Biomarker discovery in mass spectrometry-based urinary proteomics.
   Thomas S, Hao L, Ricke WA, Li L. · · 2016 · cited 113× · PMID 26703953 · DOI 10.1002/prca.201500102
2. Predictive models of sepsis-associated acute kidney injury based on machine learning: a scoping review.
   Li J, Zhu M, Yan L. · · 2024 · cited 10× · PMID 39082758 · DOI 10.1080/0886022x.2024.2380748
3. Development of a MALDI MS-based platform for early detection of acute kidney injury.
   Carrick E, Vanmassenhove J, Glorieux G, Metzger J, et al · · 2016 · cited 10× · PMID 27119821 · DOI 10.1002/prca.201500117