Impact of Inflammation After Cardiac Surgery on 30-Day Mortality and Machine Learning Risk Prediction

Abstract

Objectives

To investigate the impact of systemic inflammatory response syndrome (SIRS) on 30-day mortality following cardiac surgery and develop a machine learning model to predict SIRS.

Design

Retrospective cohort study.

Setting

Single tertiary care hospital.

Participants

Patients who underwent elective or urgent cardiac surgery with cardiopulmonary bypass (CPB) from 2016 to 2020 (N = 1,908).

Interventions

Mixed cardiac surgery operations were performed on CPB. Data analysis was made of preoperative, intraoperative, and postoperative variables without direct interventions.

Measurements and Main Results

SIRS, defined using American College of Chest Physicians/Society of Critical Care Medicine parameters, was assessed on the first postoperative day. The primary outcome was 30-day mortality. SIRS incidence was 28.7%, with SIRS-positive patients showing higher 30-day mortality (12.2% v 1.5%, p < 0.001). A multivariate logistic model identified predictors of SIRS. Propensity score matching balanced 483 patient pairs. SIRS was associated with increased mortality (OR 2.77; 95% CI 1.40-5.47, p = 0.003). Machine learning models to predict SIRS were developed. The baseline risk model achieved an area under the curve of 0.77 ± 0.04 in cross-validation and 0.73 (95% CI 0.70-0.85) on the test set, while the procedure-adjusted risk model showed improved performance with an area under the curve of 0.81 ± 0.02 in cross-validation and 0.82 (95% CI 0.76-0.85) on the test set.

Conclusions

SIRS is significantly associated with increased 30-day mortality following cardiac surgery. Machine learning models effectively predict SIRS, paving the way for future investigations on potential targeted interventions that may mitigate adverse outcomes.