Use of hematological indices in severe septic acute kidney injury to predict hospital mortality and need for renal replacement therapy at discharge

Objective: Recent studies have identified the neutrophil/lymphocyte ratio, platelet/lymphocyte ratio, and neutrophil/
lymphocyte × platelet ratio as promising prognostic markers in patients with sepsis. This study aims to evaluate the
discriminatory ability of these ratios to predict mortality and requirement for renal replacement therapy at discharge, in
patients with septic acute kidney injury.
Methods: Diagnostic test study based on a multicenter retrospective cohort of adult patients with septic acute kidney
injury requiring renal support. Hematologic ratios were calculated for three disease moments (admission, diagnosis of acute
kidney injury, initiation of renal replacement therapy). Receiver operating characteristic curves were used to analyze the
discriminative ability of the different hematological ratios at each disease moment.
Results: A total of 152 patients were included. In-hospital mortality occurred in 61.8%, and 24.2% of survivors required
renal replacement therapy at discharge. Measurements taken at the initiation of renal replacement therapy had the best
discriminatory ability to predict adverse outcomes. For neutrophil/lymphocyte ratio the area under the curve to predict
mortality was 0.596; (95% CI: 0.500–0.692), and to predict the requirement of renal replacement therapy 0.592 (95%
CI: 0.286–0.898). In all proposed scenarios, the neutrophil/lymphocyte ratio and neutrophil/lymphocyte × platelet ratio
demonstrated superior performance in comparison to the platelet/lymphocyte ratio. All three ratios exhibited comparable
poor discriminatory ability.
Conclusions: Hematological ratios have poor discriminatory capacity for predicting adverse outcomes in cases of septic
acute kidney injury. The neutrophil-to-lymphocyte ratio taken at the initiation of renal replacement therapy is a potentially
useful, economical, and easily applicable tool to be included in predictive models of mortality and dialysis dependence.