Systemic inflammatory response syndrome after cardiopulmonary bypass: prevalence, predictors, and outcomes

Systemic Inflammatory Response Syndrome (SIRS) is a frequent and critical complication following cardiac surgery, particularly in patients undergoing cardiopulmonary bypass (CPB). Although SIRS is associated with increased morbidity and mortality, its impact on hemodynamic stability and postoperative outcomes remains unclear. This study aimed to determine the prevalence of SIRS in patients undergoing cardiac surgery with CPB at the Instituto Nacional de Cardiología "Ignacio Chávez" and to explore its association with preoperative comorbidities, intraoperative factors, hemodynamic alterations, vasoactive drug requirements, and adverse outcomes.

A retrospective, observational study was conducted on 546 adult patients who underwent cardiac surgery with CPB. Patients were classified into SIRS and no SIRS groups based on standard clinical criteria. Statistical analysis included normality testing (Shapiro-Wilk), comparisons of continuous and categorical variables (Mann-Whitney U test, chi-square test, and Fisher's exact test), and logistic regression to identify predictors of outcomes.

SIRS was present in 31.86% of patients upon ICU admission. The most frequent criterion was leukocytosis (69.05%), followed by tachycardia (33.88%). Patients with SIRS exhibited elevated inflammatory markers and increased requirements for vasoactive drugs. SIRS significantly increased the odds of several postoperative complications: mediastinal bleeding (OR 2.08, CI 1.24-3.48), vasoplegic syndrome (OR 1.79, CI 0.92-3.49), low cardiac output syndrome (OR 1.71, CI 1.02-2.85), acute kidney injury (OR 1.69, CI 1.15-2.48), and in-hospital mortality (OR 1.83, CI 0.90-3.72).

SIRS is a prevalent and clinically significant condition in postoperative cardiac surgery patients. The SIRS score is as a valuable risk stratification tool. Early identification and targeted interventions may improve patient outcomes. Further prospective studies are needed to refine predictive models and optimize management strategies.