Unsupervised machine learning to explore inflammation following cardiopulmonary bypass

IntroductionCardiac surgery with cardiopulmonary bypass (CPB) often induces systemic inflammatory reaction syndrome (SIRS), affecting postoperative outcome. We aimed to explore adaptive/maladaptive inflammation using unsupervised machine learning.MethodsWe conducted a post hoc analysis of 1908 adult patients who underwent elective cardiac surgery with CPB between June 2016 and June 2020 at a single institution. Patients were assessed for SIRS 12 hours post-surgery and clustered using the partitioning around medoids (PAM) algorithm based on Gower distance. The influence of SIRS on a composite outcome comprising death, stroke/TIA, renal replacement therapy, reoperation for bleeding, mechanical circulatory support, and ICU stay >96 hours was analyzed via multivariable logistic regression.ResultsSIRS occurred in 28.7% of patients (median age 69 years; 68.7% male). Clustering revealed two subgroups: maladaptive SIRS (52.9%) with higher preoperative risk and worse outcomes, and adaptive SIRS (47.1%) with favorable outcomes. Maladaptive SIRS patients had higher 30-day mortality (21.7% vs 1.6%, p < .001). Adaptive SIRS patients had outcomes similar to SIRS-negative controls. In selected clusters, SIRS was independently associated with a lower risk of the composite outcome (OR 0.44; 95% CI 0.26-0.74, p = .002).ConclusionUnsupervised machine learning effectively identifies adaptive and maladaptive SIRS in cardiac surgery patients, providing a basis for personalized postoperative care. Several clinical and procedural factors associated with maladaptive SIRS may be modifiable, supporting future precision strategies to reduce harmful inflammation after cardiac surgery.