Normalized lactate load is associated with development of acute kidney injury in patients who underwent cardiopulmonary bypass surgery

Abstract

Background and objective: Cardiac surgery associated acute kidney injury is a major postoperative complication and has long been associated with adverse outcomes. However, the association of lactate and AKI has not been well established. The study aimed to explore the association of normalized lactate load with AKI in patients undergoing cardiac surgery.

Methods: This was a prospective observational cohort study conducted in a 47-bed ICU of a tertiary academic teaching hospital from July 2012 to January 2014. All patients undergoing cardiopulmonary bypass surgery were included. Normalized lactate load (L) was calculated by the equation: [Formula: see text], where ti was time point for lactate measurement and vi was the value of lactate. L was transformed by natural log (Lln) to improve its normality. Logistic regression model was fitted by using stepwise method. Scale of Lln was examined by using fractional polynomial approach and potential interaction terms were explored.

Results: A total of 117 patients were included during study period, including 17 AKI patients and 100 non-AKI patients. In univariate analysis Lln was significantly higher in AKI as compared with non-AKI group (1.43±0.38 vs 1.01±0.45, p = 0.0005). After stepwise selection of covariates, the main effect logistic model contained variables of Lln (odds ratio: 11.1, 95% CI: 1.22-101.6), gender, age, baseline serum creatinine and fluid balance on day 0. Although the two-term fractional polynomial model was the best-fitted model, it was not significantly different from the linear model (Deviance difference = 6.09, p = 0.107). There was no significant interaction term between Lln and other variables in the main effect model.

Conclusions: Our study demonstrates that Lln is independently associated with postoperative AKI in patients undergoing CPB. There is no significant interaction with early postoperative fluid balance.

Fig 1. Schematic illustration of the calculation of lactate load and normalized lactate load.

Fig 2. Histogram showing the number of lactate measurements within the first 24 hours.

Fig 3. Graphical presentation of the fitted two-term (3 3) fractional polynomial logistic regression model.

In the upper panel, the y-axis is in logit scale with the advantage of better distribution for model fit. Y-axis is transformed to the probability of AKI in lower panel which is more comprehensible to subject matter audience. The plots show that the probability of AKI increases progressively with increasing normalized lactate load, reaching its peak at L_ln≈1.4.

Fig 4. Graphical presentation of model discrimination.

Jittered plot shows that non-AKI dots are mostly clustered to the left side and AKI dots are mostly clustered to the right side. ROC curve showed good discriminating power of the model with an area under ROC of 0.84. The histogram shows the distribution of predicted risk score for AKI stratified by observed AKI and non-AKI. In non-AKI group, the distribution of predicted AKI probability skewed to the right; while in AKI group, the distribution of predicted AKI probability skewed to the left. P = 0.2707 for Hosmer-Lemeshow goodness-of-fit test.

Fig 5. Receiver operating characteristic curve of the initial lactate to predict AKI development.

The area under curve was 0.63.