Acute kidney injury in an internal medicine ward in a Portuguese quaternary hospital

• We compare RIFLE, AKIN and KDIGO guidelines for AKI in an internal medicine ward.
• RIFLE, AKIN and KDIGO classifications showed a good measure of agreement.
• RIFLE, AKIN and KDIGO showed a statistically significant positive correlation.
• New biomarkers will permit earlier identification of renal dysfunction.

BackgroundThe term acute kidney injury (AKI) was proposed to reflect the wide spectrum of traditional acute renal failure. RIFLE classification stratifies AKI into three classes of severity and two classes of outcome. AKIN classification proposes an improvement regarding RIFLE in the stratification of AKI, while recently published KDIGO guidelines comprise characteristics of both RIFLE and AKIN. There are no published studies on the utility and measure of agreement between classifications in patients admitted to internal medicine wards.MethodsProspective study undertaken in two internal medicine wards in a Portuguese hospital. Patients admitted for a minimum of 72 h, with a diagnosis of AKI or acute-on-chronic kidney disease at admission or during hospitalisation, were included. RIFLE, AKIN and KDIGO criteria were applied for identification of AKI and stratification into risk groups.ResultsSixty-nine patients were included, with a mean age of 79.7 ± 10.0 years and mean GFR of 21.7 ± 8.8 mL/min/1.73 m2. Hypovolaemia due to dehydration was the main cause of AKI (53.6%) and, thereby, RIFLE classification identified a higher number of patients as having AKI, compared to AKIN (94.2% vs. 84.1%). Most patients (69.6%) recovered to their baseline renal function, however fifteen patients (21.7%) died, 53.3% presenting more severe kidney disease.ConclusionsOur results demonstrate good concordance and correlation between RIFLE, AKIN and KDIGO criteria for the diagnosis of AKI (p < 0.001 at initial and final assessment). The authors support the need for further improvement of the classification, ultimately through the use of new biomarkers capable of earlier identification of patients at risk.