NCT06706323

Depressive Symptoms After Cardiac Surgery

Predictive Modelling of Clinically Significant Depressive Symptoms After Coronary Artery Bypass Graft Surgery

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 300 (estimated)
• Sponsor: Roland von Känel · Academic / Other
• Sex: All
• Age: 18 Years – 90 Years
• Healthy volunteers: —

Summary

The primary goal of this project is to develop a predictive model for clinically significant depressive symptoms (CSDS) in patients undergoing coronary artery bypass graft (CABG) surgery, using pre- and perioperative data. CSDS occur in about 30 percent of CABG patients, which is four times higher than in the general population. These symptoms are linked to poor quality of life and increased morbidity and mortality. The aim is to create a model that can identify patients at risk for postoperative depression. This tool could help clinicians make informed decisions and take preventive measures to manage depression after surgery.

Detailed description

In patients undergoing coronary artery bypass graft (CABG) surgery, the prevalence of clinically significant depressive symptoms (CSDS) is about 30 percent, four times higher than the 12-month prevalence in the general population. CSDS are associated with poor quality of life and increased morbidity and mortality. While several predictors of post-CABG CSDS have been identified, no prognostic model exists. The aim of this project is to develop a predictive model for post-surgery CSDS in CABG patients using pre- and perioperative data. A prognostic prediction model for CSDS 6 weeks post-CABG, will be developed using demographic, psychometric, medical, inflammation, and cardiac interoception data. Machine learning algorithms will be employed for data analysis. A cohort of 350 participants from two hospitals will be recruited, with 300 participants expected to complete the study. Data will be divided into training (200 participants) and testing (100 participants) sets. Nested cross-validation will prevent overfitting. Both binary and regression prediction models will be used. Additionally, a simpler model will be developed to increase generalizability. The prediction model will identify CABG patients at risk of post-surgery CSDS. The model will help identify patients at risk for CSDS before surgery, enabling early interventions. Clinicians can make precision medicine decisions to prevent or manage CSDS, improving postoperative psychological well-being. Additionally, the study could advance understanding of the mechanisms linking depression and coronary heart disease, particularly in relation to inflammation and interoception.

Conditions

• Cardiovascular Diseases
• Coronary Artery Disease
• Heart Diseases
• Depression
• Depressive Symptoms
• Anxiety
• Posttraumatic Stress Disorder
• Quality of Life
• Psychological Well-Being
• Postoperative Complications

Timeline

• Start date: 2024-11-25
• Primary completion: 2027-04-01
• Completion: 2027-08-31
• First posted: 2024-11-26
• Last updated: 2025-04-01

Locations

2 sites across 1 country: Switzerland