Please use this identifier to cite or link to this item: http://ahro.austin.org.au/austinjspui/handle/1/22599
Title: Point-of-care creatinine measurements to predict acute kidney injury.
Authors: Vaara, Suvi T;Glassford, Neil J;Eastwood, Glenn M;Canet, Emmanuel;Mårtensson, Johan;Bellomo, Rinaldo
Affiliation: Centre for Integrated Critical Care, Department of Medicine & Radiology, Melbourne Medical School, The University of Melbourne, Melbourne, Australia
Division of Intensive Care Medicine, Department of Anesthesiology, Intensive Care and Pain Medicine, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
Intensive Care Unit, Royal Melbourne Hospital, Melbourne Health, Melbourne, Australia
Department of Epidemiology and Preventative Medicine, School of Public Health and Preventative Medicine, Monash University, Melbourne, Australia
Department of Intensive Care, Austin Health, Heidelberg, Victoria, Australia
Intensive Care Unit, Nantes University Hospital, University of Nantes, France
Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
Issue Date: 14-Feb-2020
EDate: 2020-02-14
Citation: Acta anaesthesiologica Scandinavica 2020; online first: 14 February
Abstract: Plasma creatinine (Cr) is a marker of kidney function and typically measured once daily. We hypothesized that Cr measured by point-of-care technology early after ICU admission would be a good predictor of acute kidney injury (AKI) the next day in critically ill patients. We conducted a retrospective database audit in a single tertiary ICU database. We included patients with normal first admission Cr (CrF ) and identified a Cr value (CrP ) obtained within 6 to 12 hrs from ICU admission. We used their difference converted into percentage (delta-Cr-%) to predict subsequent AKI (based on Cr and/or need for renal replacement therapy) the next day. We assessed predictive value by calculating area under the receiver characteristic curve (AUC), logistic regression models for AKI with and without delta-Cr-%, and the category-free net reclassifying index (cfNRI). We studied 780 patients. Overall, 70 (9.0%) fulfilled the Cr AKI definition by CrP measurement. On day 2, 148 patients (19.0%) were diagnosed with AKI. AUC (95% CI) for delta-Cr-% to predict AKI on day 2 was 0.82 (95% CI 0.78-0.86), and 0.74 (95% CI 0.69-0.80) when patients with AKI based on the CrP were excluded. Using a cut-off of 17% increment, the positive likelihood ratio (95% CI) for delta-Cr-% to predict AKI was 3.5 (2.9 - 4.2). The cfNRI was 90.0 (74.9-106.1). Among patients admitted with normal Cr, early changes in Cr help predict AKI the following day.
URI: http://ahro.austin.org.au/austinjspui/handle/1/22599
DOI: 10.1111/aas.13564
ORCID: 0000-0001-8739-7896
0000-0002-1650-8939
PubMed URL: 32057092
Type: Journal Article
Appears in Collections:Journal articles

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