Abstract
Background. Outcomes after acute kidney injury (AKI) are well described, but not for those already under nephrology clinic care. This is where discussions about kidney failure risk are commonplace. We evaluated whether the established kidney failure risk equation (KFRE) should account for previous AKI episodes when used in this setting.Methods. This observational cohort study included 7491 peoplereferred for nephrology clinic care in British Columbia in 2003–09followed to 2016. Predictors were previous Kidney Disease: Improving Global Outcomes–based AKI, age, sex, proteinuria, estimated glomerular filtration rate (eGFR) and renal diagnosis.Outcomes were 5-year kidney failure and death. We developedcause-specific Cox models (AKI versus no AKI) for kidney failureand death, stratified by eGFR (</30 mL/min/1.73 m2). We alsocompared prediction models comparing the 5-year KFRE withtwo refitted models, one with and one without AKI as a predictor.Results. AKI was associated with increased kidney failure (33.1%versus 26.3%) and death (23.8% versus 16.8%) (P < 0.001). InCox models, AKI was independently associated with increased kidney failure in those with an eGFR 30 mL/min/1.73 m2 {hazard ratio [HR] 1.35 [95% confidence interval (CI) 1.07–1.70]}, no increase in those with eGFR <30 mL/min/1.73 m2 ([HR 1.05 95% CI 0.91–1.21)] and increased mortality in both subgroups [respective HRs 1.89 (95% CI 1.56–2.30) and 1.43 (1.16–1.75)]. Incorporating AKI into a refitted kidney failure prediction model did not improve predictions on comparison of receiver operating characteristics (P¼ 0.16) or decision curve analysis. The original KFRE calibrated poorly in this setting, underpredicting risk.Conclusions. AKI carries a poorer long-term prognosis amongthose already under nephrology care. AKI may not alter kidneyfailure risk predictions, but the use of prediction models withoutappreciating the full impact of AKI, including increased mortality, would be simplistic. People with kidney diseases have risksbeyond simply kidney failure. This complexity and variability ofoutcomes of individuals is important.
Original language | English |
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Pages (from-to) | 836-845 |
Number of pages | 10 |
Journal | Nephrology Dialysis Transplantation |
Volume | 35 |
Issue number | 5 |
Early online date | 15 Oct 2018 |
DOIs | |
Publication status | Published - 1 May 2020 |
Bibliographical note
ACKNOWLEDGEMENTSWe are grateful to Dr Nadia Zalunardo for her comments on this study.
FUNDING
Dr Sawhney received funding from a research training fellowship from the Wellcome Trust to conduct this study (102729/
Z/13/Z).
Keywords
- acute kidney injury
- epidemiology
- kidney failure
- prognosis
- prediction
- DISEASE
- MODEL
- OUTCOMES
- PROGRESSION
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Simon Sawhney
- School of Medicine, Medical Sciences & Nutrition, Applied Health Sciences - Senior Clinical Lecturer
- School of Medicine, Medical Sciences & Nutrition, Aberdeen Centre for Health Data Science
- School of Medicine, Medical Sciences & Nutrition, Farr Aberdeen
- School of Medicine, Medical Sciences & Nutrition, Grampian Data Safe Haven (DaSH)
Person: Clinical Academic