Backgrounds: Criteria that may guide early renal replacement therapy (RRT) initiation in patients with acute kidney injury (AKI) currently do not exist.
Methods: In 120 consecutive patients with AKI, clinical and laboratory data were analyzed on admittance. The prognostic power of those parameters which were significantly different between the two groups was analyzed by receiver operator characteristic curves and by leave-1-out cross validation.
Results: Six parameters (urine albumin, plasma creatinine, blood urea nitrogen, daily urine output, fluid balance and plasma sodium) were combined in a logistic regression model that estimates the probability that a particular patient will need RRT. Additionally, a second model without daily urine output was established. Both models yielded a higher accuracy (89 and 88% correct classification rate, respectively) than the best single parameter, cystatin C (correct classification rate 74%).
Conclusions: The combined models may help to better predict the necessity of RRT using clinical and routine laboratory data in patients with AKI.
Keywords: Acute kidney injury; Dialysis; Logistic regression model; Renal failure; Renal replacement therapy.