Unsupervised machine learning to explore inflammation following cardiopulmonary bypass.

Journal: Perfusion
Published Date: Aug 27, 2025

Abstract

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.

Authors

  • Enrico Squiccimarro
    Division of Cardiac Surgery, Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy; Cardio-Thoracic Surgery Department, Heart & Vascular Centre, Maastricht University Medical Centre, Maastricht, The Netherlands.
  • Roberto Lorusso
    Cardio-Thoracic Surgery Department, Heart & Vascular Centre, Maastricht University Medical Centre, Maastricht, The Netherlands; Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands.
  • Paolo Vetuschi
    Division of Cardiac Anesthesia, Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy.
  • Michela Rauseo
  • Gianluca Paternoster
    Department of Health Science Anesthesia and ICU, San Carlo Hospital, University of Basilicata, Potenza, Italy.
  • Giuseppe Speziale
    Department of Cardiac Surgery, Santa Maria Hospital-GVM Care & Research, Bari, Italy.
  • Richard P Whitlock
    Division of Cardiac Surgery, Department of Surgery, McMaster University, Hamilton, ON, Canada; Population Health Research Institute, Hamilton, ON, Canada.
  • Domenico Paparella
    Division of Cardiac Surgery, University of Bari, Bari, Italy.

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