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August 19, 2025Anesthesia & Analgesia

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

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Authors

ADAndra E. DuncanKSKaran ShahMYManshu Yan

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Overview

Analysis of treatment-effect heterogeneity identified clinical phenotypes in cardiac surgery patients, suggesting targeted approaches may enhance outcomes.

Key Points

  • Methylprednisolone showed no significant variation in treatment effectiveness across clinical phenotypes, indicating consistent outcomes among different patient groups.
  • Younger and healthier patients had the lowest risk, while the sickest group faced a 3.4-fold higher odds of severe outcomes compared to the healthiest phenotype.
  • Using the partitioning around medoids algorithm, 16,395 patients were clustered into four distinct phenotypes based on clinical profiles and surgical interventions.
  • The neutral outcomes of methylprednisolone treatment across diverse phenotypes highlight the need for further exploration of personalized treatment strategies.

Cite This Study

Duncan et al. (2025) studied this question.

synapsesocial.com/papers/68af35ddcf1dd9ea359e9bdahttps://doi.org/10.1213/ane.0000000000007715
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Methylprednisolone and Endothelial Dysfunction in Cardiac Surgery: A Pilot Randomized Trial2025 · 1 citations
  2. 2Effectiveness of Corticosteroid Strategy in Three Community‐Acquired Pneumonia Clinical Phenotypes: A Multicenter Retrospective Cohort Study2025
  3. 3Large-scale proteomic profiling identifies distinct inflammatory phenotypes in Acute Respiratory Distress Syndrome (ARDS): A multi-center, prospective cohort study2025
  4. 4Acute changes in indices of cardiac filling and function induced by high-dose intravenous methylprednisolone pulse therapy: a single-center prospective study2025
  5. 5Evaluation of steroids for acute COVID in the prevention of long COVID in children: An EHR and pediatric cohort study from the RECOVER Initiative2026