Ms Nila Kirubakaran1, Dr Sanjeeva Herath1,2, Dr Amy Au1,2, Dr Kylie Taylor1, Dr Jonathan Erlich1,2, Professor Zoltan Endre1,2
1University Of New South Wales, Kensington, Australia, 2Prince of Wales Hospital, Randwick, Australia
Presentation Slides PDF – Click Here
Biography:
Dr Jonathan Erlich’s work has focused on how cell signaling contributes to inflammation in a variety of different scenarios.
Current work has largely focused on renal ischaemia reperfusion injury (IRI). The studies aim to discover and validate biomarkers and define potential therapeutic targets that may limit and or potentially treat the problem of renal damage that occurs following renal IRI. This scenario is relevant to a wide range of clinical situations including aortic surgery and renal transplantation where renal IRI can be predicted. Dr Erlich is also the head of nephrology department at Prince of Wales hospital.
Aim:
We evaluated the added value of novel urinary and plasma biomarkers over the current best clinical risk prediction models for predicting 1- and 4-year mortality in patients with high pretest probability of AKI admitted via the emergency department (ED).
Background:
Implementation of novel biomarkers in heterogenous populations is complex. Determining the clinical validity of novel biomarkers in select cohorts is hence pivotal.
Methods:
This prospective, single center study enrolled 166 ED patients with >30% risk of developing acute kidney injury (AKI) by nephrologist judgement based on predefined criteria (ED score). Admission urine and plasma samples were assayed for 18 biomarkers. Urinary biomarkers were normalised to urinary creatinine. AKI risk score (AKI-RiSc) was calculated for each patient based on age and comorbidities. Model performance was evaluated using area under the curve (AUC) to determine the added benefit of each biomarker to the AKI-RiSc for prediction of mortality.
Results:
Accuracy of ED score alone for selecting AKI-KDIGO was 30%. AUC of AKI-RiSc 5 for predicting AKI was 0.75 (95% CI 0.69-0.81). Urine kidney injury molecule (KIM)-1 was the best performing biomarker when added to AKI-RiSc for predicting mortality (based on generalized R squared and Akaike information criterion). Addition of KIM-1 to AKI-RiSc improved prediction of 1-year mortality with AUC 0.77 (95% CI: 0.64 -0.86) versus 0.67 (95% CI: 0.50-0.75, p<0.05) for AKI-RiSc 5 alone, and also improved prediction of 4-year mortality with AUC of 0.77 (95% CI: 0.67-0.84) versus 0.67 (95% CI: 0.59-0.75, p<0.01) for AKI-RiSc 5 alone.
Conclusion:
Addition of urine KIM-1 to the AKI risk score improved prediction of 1- and 4-year mortality in ED patients at high-risk of AKI.