ASSOCIATION OF SYSTEMIC INFLAMMATORY REACTION AFTER CARDIAC SURGERY WITH INCREASED 30-DAY MORTALITY: A MACHINE LEARNING APPROACH FOR RISK PREDICTION

Background and Aim: Systemic inflammation (SIRS) characterizes the postoperative course of cardiac surgery, worsening patient outcomes in its most relevant form. We investigated the impact of SIRS on 30-day mortality and developed a machine-learning model for SIRS prediction. Methods: A retrospective evaluation of patients who underwent cardiac surgery from 2016- 2020 in a single hospital was performed. SIRS was assessed 12 hours post-surgery using the ACCP/SCCM criteria. A multivariate logistic model identified SIRS predictors. SIRS-positive patients were matched 1:1 with SIRS-negative ones. The effect of SIRS on mortality was investigated within the propensity-matched cohort. Baseline Risk (BRM) and Procedure- adjusted Risk (PARM) Random Forest Models were trained, optimized, and calibrated. Models’ performance was assessed via cross-validation (CV), using the AUC as a benchmark metric. Results: A total of 1908 patients were included. SIRS incidence was 28.7%, and 30-day mortality was 4.6%. Propensity scoring matched 483 patient pairs. SIRS was significantly associated with mortality (OR 2.77; 95%CI 1.40-5.47, p=0.003). SIRS mediated a proportion of its intraoperative predictors’ impact on mortality: 24.3% for anemia, 9.9% for vasopressors, 4.0% for hyperlactatemia (p<0.001). The BRM produced an AUC 0.77±0.04 in the 5-fold CV and an AUC 0.73 (95%CI 0.70-0.85) on the test set, whereas the PRM culminated in an AUC 0.81±0.02 in the CV and 0.82 (95%CI 0.76-0.85) on the test-set (DeLong p<0.001). Conclusion: Clinically-defined SIRS after cardiac surgery is associated with 30-day mortality. Machine learning allows to predict SIRS effectively, and paves the way for research exploring targeted interventions to prevent/mitigate its negative effects.