Combination of Mean Platelet Volume/Platelet Count Ratio and the APACHE II Score Better Predicts the Short-Term Outcome in Patients with Acute Kidney Injury Receiving Continuous Renal Replacement Therapy.

Background/Aims: Both the Acute physiology and Chronic Health Evaluation (APACHE II) score and mean platelet volume/platelet count Ratio (MPR) can independently predict adverse outcomes in critically ill patients. This study was aimed to investigate whether the combination of them could have a better performance in predicting prognosis of patients with acute kidney injury (AKI) who received continuous renal replacement therapy (CRRT). Methods: Two hundred twenty-three patients with AKI who underwent CRRT between January 2009 and December 2014 in a Chinese university hospital were enrolled. They were divided into survivals group and non-survivals group based on the situation at discharge. Receiver Operating Characteristic (ROC) curve was used for MPR and APACHE II score, and to determine the optimal cut-off value of MPR for in-hospital mortality. Factors associated with mortality were identified by univariate and multivariate logistic regression analysis. Results: The mean age of the patients was 61.4 years, and the overall in-hospital mortality was 48.4%. Acute cardiorenal syndrome ACRS) was the most common cause of AKI. The optimal cut-off value of MPR for mortality was 0.099 with an area under the ROC curve (AUC) of 0.636. The AUC increased to 0.851 with the addition of the APACHE II score. The mortality of patients with of MPR > 0.099 was 56.4%, which was significantly higher than that of the control group with of <= 0.099 (39.6%, P = 0.012). Logistic regression analysis showed that average number of organ failure (OR = 2.372), APACHE II score (OR = 1.187), age (OR = 1.028) and vasopressors administration (OR = 38.130) were significantly associated with poor prognosis. Conclusion: Severity of illness was significantly associated with prognosis of patients with AKI. The combination of MPR and APACHE II score may be helpful in predicting the short-term outcome of AKI. (C) 2018 The Author(s) Published by S. Karger AG, Basel.