Predictive modelling of clinically significant depressive symptoms after coronary artery bypass graft surgery:

Asimina Lazaridou1, Sinthujan Sivakumar2, Hector Rodriguez Cetina Biefer3,4,5

  • 1Brigham and Women's Hospital, Chestnut Hill, Massachusetts, USA.

BMJ Open
|September 5, 2025
PubMed

Insights

This study develops predictive models to identify patients at risk of depression after coronary artery bypass grafting (CABG) surgery. Early identification of psychological distress can improve patient outcomes and inform targeted interventions.

Area of Science:

  • Cardiology
  • Psychiatry
  • Medical Informatics

Background:

  • Coronary artery bypass grafting (CABG) is a common cardiac surgery.
  • Post-CABG psychological distress, particularly depression, is prevalent.
  • Predicting and managing this distress requires better tools.

Purpose of the Study:

  • Develop and validate prognostic models to predict clinically significant depressive symptoms (CSDSs) after CABG.
  • Identify key predictors of psychological morbidity, including anxiety and PTSD.
  • Improve perioperative mental healthcare through early risk identification.

Main Methods:

  • Multicentre observational study of 300 adult patients undergoing elective CABG.
  • Machine learning models developed on a training set (n=200) and validated on a test set (n=100).
  • Data includes demographics, clinical, psychometric, inflammation, and interoceptive variables; primary outcome: CSDSs (PHQ-9).

Main Results:

  • Predictive models for CSDSs, anxiety, PTSD symptoms, and quality of life will be generated.
  • A simplified 'light solution' model will be developed for wider use.
  • Key predictors of post-CABG psychological outcomes will be identified.

Conclusions:

  • This research addresses a critical gap in identifying patients at risk for psychological distress post-CABG.
  • The developed models can inform screening and preventive strategies.
  • Early intervention based on these models may enhance postsurgical outcomes.
Abstract

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