depressive symptoms after cardiac surgery

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

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.

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.

Eligibility criteria:
inclusion criteria: * elective off-pump cabg or cabg, either isolated or combined with valve intervention * men and women, aged between 18 and 90 years * sufficient knowledge of german language in reading and understanding * oral and signed consent form * ability and willingness to follow the study protocol exclusion criteria: * cognitive impairment according to a score of ≤ 7 (maximum score = 9) on a modified version of a short version of the mini-mental state examination and the brief interview for mental status * any serious comorbid non-cardiac medical condition likely to cause death within 1 year (e.g. metastatic cancer) * active psychotic symptoms (assessed with two items from the youth psychosis at risk questionnaire), substance abuse and/or dependence within the past 6 months (assessed with a single-item questionnaire), and/or active suicidal ideations (assessed with a single item from the m.i.n.i.). * acute or emergency cabg

Keywords: coronary artery bypass graft coronary artery bypass grafting coronary artery bypass surgery cabg inflammation interoception clinically significant depressive symptoms csds patient health questionnaire-9 phq-9 postoperative depression algorithms prediction predictive modeling machine learning