Predicting depression in patients after heart surgery
Official title: Predictive Modelling of Clinically Significant Depressive Symptoms After Coronary Artery Bypass Graft Surgery
Run by University of Zurich · NCT06706323 · Data updated 2026-11-12
This study is trying to see if a new tool can help predict which patients might develop depression after heart surgery, so they can get help sooner.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years to 90 Years |
| Sex | All |
| Enrollment | 300 (estimated) |
| Sponsor | University of Zurich Academic / other |
| Locations | 2 sites (Zurich and 1 other locations) |
| Status | Recruiting |
| Started | November 25, 2024 |
| Main results expected | April 1, 2027 |
| Study ends | August 31, 2027 |
| Registry record updated | April 1, 2025 |
| Trial ID | NCT06706323 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates for this study are adults aged 18 to 90 who are scheduled for elective CABG surgery and can understand German.
✕Probably not a fit
Patients with serious comorbid conditions, cognitive impairments, or active psychotic symptoms may not benefit from this study.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This project aims to develop a predictive model for clinically significant depressive symptoms (CSDS) in patients undergoing coronary artery bypass graft (CABG) surgery. By analyzing pre- and perioperative data from a cohort of 350 participants, the study will utilize machine learning algorithms to identify patients at risk for postoperative depression. The model will incorporate various data types, including demographic, psychometric, medical, and inflammation data, to enhance its predictive accuracy. Ultimately, the goal is to enable early interventions and improve the psychological well-being of CABG patients.
Why it matters
Potential benefit: If successful, this model could help clinicians identify and manage depression in CABG patients, leading to improved postoperative outcomes.
How similar studies have gone: While there have been studies on predictors of depression in cardiac patients, this specific predictive modeling approach using machine learning is relatively novel.
Who can join: the full rules
Show full inclusion / exclusion 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
Where it's running
-
Stadtspital Zürich (City Hospital Zurich) Triemli
Zurich, Switzerland Recruiting -
University Hospital Zurich (USZ)
Zurich, Switzerland Not yet recruiting
Study contacts
- Principal investigator: Omer Dzemali, Prof. Dr. — Stadtspital Zürich Triemli, Klinik für Herzchirurgie, Birmensdorferstr. 497, 8063 Zurich, Switzerland
- Study coordinator: Roland v Känel, Prof. Dr.
- Email: roland.vonkaenel@usz.ch
- Phone: +41 (0)44 255 52 51
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.