Perioperative Changes in Plasma Nitrite and IL-6 Levels Predict Postoperative Atrial Fibrillation (POAF) and Acute Kidney Injury (AKI) after Cardiac Surgery

Abstract

Background: Postoperative atrial fibrillation (POAF) and acute kidney injury (AKI) are common yet significant complications after cardiac surgery, with incidences of up to 40% for each. Here, we assessed plasma nitrite and serum interleukin-6 (IL-6) levels before and after cardiac surgery to quantify the extent to which oxidative stress and inflammation contribute to POAF and AKI occurrence. Methods: We prospectively enrolled 206 cardiac surgical patients. Plasma nitrite and serum IL-6 levels were determined preoperatively and at 24 h, 48 h and 72 h postoperatively. The patients had continuous EKG monitoring for occurrence of POAF, while daily serum creatinine was measured for determination of stage 1 + AKI. Results: Postoperatively, 78 (38%) patients experienced AF, and 47 (23%) patients experienced stage 1 + AKI. POAF analysis: Age, ACE-inhibitor use, valve surgery and percent change in baseline plasma nitrite at 24 h postoperatively were associated with POAF in multiple logistic regression analysis. The inclusion of this new biomarker significantly improved the POAF prediction model (AUC 0.77 for clinical risk factors alone, to AUC 0.81). AKI analysis: A history of diabetes mellitus was associated with AKI in multiple logistic regression analysis, and the addition of preoperative IL-6 levels improved the prediction model for AKI occurrence (AUC 0.69 to AUC 0.74). Conclusions: We previously observed selective upregulation of NADPH oxidase isoform 4 (NOX4) in patients with AF, a critical causal role of NOX4 for AF in zebrafish and a robust inhibitory effect of nitric oxide (NO) on NOX4. Our data innovatively demonstrate that a reduction in circulating nitrite levels, likely implicative of elevated NOX4-mediated oxidative stress, independently associates with POAF and improves POAF prediction, whereas the inclusion of circulating IL-6 levels improves the prediction model for AKI. Therefore, therapeutic strategies to mitigate these pathophysiological sequalae of surgical stress may reduce the incidence of severe postoperative complications of POAF and AKI.

Keywords: ROS; acute kidney injury; atrial fibrillation; nitric oxide (NO); nitrite; post-operative complications.

Figure 1

Percent Change from Baseline Plasma Nitrite Level by POAF Occurrence. These line plots demonstrate the mean percent change in baseline plasma nitrite level through postoperative days 1, 2 and 3 for patients who experience POAF and those who do not. The error bars display the standard error of the mean. The mean percentage change in plasma nitrite level from baseline is statistically significant through postoperative day 1 (p-value = 0.033) but not through postoperative days 2 or 3. Statistically significant results are indicated by *.

Figure 2

Biomarker Level by POAF Occurrence. These box plots demonstrate the association of preoperative BNP levels (p-value = 0.001) and postoperative day 2 IL-6 levels (p-value = 0.126) with POAF occurrence. Statistically significant results indicated by *.

Figure 3

ROC Curve for POAF Prediction Model. ROC curves are shown for the POAF prediction model in Table 5 (age, ACE inhibitor use, valve surgery, IL-6 level at 48 h and percent change from baseline plasma nitrite levels in the first 24 h) and a POAF prediction model with clinical predictors only (age, ACE inhibitor use and valve surgery). The AUC for the clinical model is 0.77 (95% CI 0.75–0.79) and the AUC for the clinical model with IL-6 at 48 h + %Δ Nitrite at 24 h is 0.81 (95% CI 0.79–0.83). The improvement in AUC for the clinical model with biomarkers is statistically significant by DeLong’s test with p-value = 0.0093.

Figure 4

Biomarker Level by AKI Occurrence. These box plots demonstrate the association of preoperative IL-6 levels (p-value < 0.001), postoperative day 3 IL-6 levels (p-value = 0.002) and preoperative BNP levels (p-value < 0.001) with stage 1 or greater AKI occurrence. Statistically significant results are indicated by *.

Figure 5

ROC Curve for AKI Prediction Model. ROC curves are shown for the AKI prediction model in Table 8 (diabetes mellitus, preoperative IL-6 level and cardiopulmonary bypass time) and an AKI prediction model with clinical predictors only (diabetes mellitus and cardiopulmonary bypass time). The AUC for the clinical model is 0.69 (95% CI 0.66–0.72) and the AUC for the clinical model with IL-6 is 0.74 (95% CI 0.71–0.77). The improvement in AUC for the clinical model with biomarkers is statistically significant by DeLong’s test with p-value = 0.016.