Use of Machine Learning to Derive Discrete Clinical Phenotypes and Assess Treatment-Effect Heterogeneity in the Steroids in Cardiac Surgery Trial Dataset.

Journal: Anesthesia and analgesia
Published Date: Aug 19, 2025

Abstract

BACKGROUND: Robust clinical trial data provide a key component for the development of evidence-informed medicine. However, clinical trial data may demonstrate treatment-effect heterogeneity, where some patients benefit from an intervention while others receive no benefit or perhaps even harm. If so, targeted therapy or a "personalized medicine" approach could provide treatment to a certain patient subset, that is, a specific clinical phenotype, who are most likely to benefit. Using data from the Steroids in Cardiac Surgery (SIRS) clinical trial, we tested the hypothesis that methylprednisolone, which did not have a significant effect on mortality or major morbidity, improves outcomes in 1 or more clinical phenotypes.

Authors

  • Andra E Duncan
    Department of Anesthesiology Cleveland Clinic Foundation Cleveland, Ohio.
  • Karan Shah
    Outcomes Research® Consortium, Houston, Texas.
  • Manshu Yan
    From the Department of Cardiothoracic Anesthesia, Cleveland Clinic, Cleveland, Ohio.
  • Nakul S Kumar
    Department of Intensive Care and Resuscitation, Cleveland Clinic, Cleveland, Ohio.
  • Daniel I Sessler
    Michael Cudahy Professor & Chair, Department of OUTCOMES RESEARCH, Anesthesiology Institute, Cleveland Clinic, Cleveland, Ohio, USA.
  • Richard P Whitlock
    Division of Cardiac Surgery, Department of Surgery, McMaster University, Hamilton, ON, Canada; Population Health Research Institute, Hamilton, ON, Canada.

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