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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.
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.
Introduction:
Coronary artery bypass grafting (CABG) remains one of the most commonly performed cardiac surgeries worldwide. Despite surgical advancements, a significant proportion of patients experience psychological distress following surgery, with depression being particularly common. Current evidence regarding the effectiveness of preoperative psychological interventions in improving postoperative mental health outcomes remains inconclusive. There is a critical need for predictive models that can identify patients at risk of developing clinically significant depressive symptoms (CSDSs) and related psychological conditions after CABG. This multicentre observational study aims to develop and validate prognostic models for predicting CSDSs and other psychological outcomes, including anxiety, post-traumatic stress symptoms and quality of life, 6 weeks after elective CABG surgery.
Methods And Analysis:
The study will recruit 300 adult patients undergoing elective CABG (with or without valve intervention) across two Swiss hospitals. Data collected will include demographic, clinical, psychometric, inflammation-related and interoceptive variables. A training set (n=200) will be used to develop predictive models using machine learning, while a held-out test set (n=100) will be used for model validation. The primary outcome prediction will focus on CSDSs, assessed using the Patient Health Questionnaire-9 (PHQ-9), with analyses conducted both categorically (PHQ-9 total score ≥10) and continuously as complementary approaches. Secondary models will address anxiety, using the General Anxiety Disorder Scale-7, post-traumatic stress, using the post-traumatic stress disorder checklist for Diagnostic and Statistical Manual of Mental Disorders-5 and health-related quality of life, using the 12-item Short Form Survey. A simplified 'light solution' model with fewer predictors will also be developed for broader applicability. This study will address an important gap in perioperative mental healthcare by identifying key predictors of psychological morbidity following CABG, particularly CSDSs. The resulting models may inform future screening and preventive strategies and improve postsurgical outcomes through early identification and intervention in high-risk individuals.
Ethics And Dissemination:
The responsible ethics committee has reviewed and approved this project (Kantonale Ethikkommission Zürich, BASEC number: 2023-02040). The study minimises participant burden by integrating brief validated instruments and limiting psychiatric interviews to relevant outcomes, while ensuring ethical safeguards and respect for participant rights (including written consent). Results will be shared through peer-reviewed publications, conference presentations and stakeholder meetings involving clinicians and mental health professionals. Findings will also be communicated to participating centres and patient communities in accessible formats.
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