Major Adverse Cardiovascular and Cerebrovascular Events (MACCE)
Conditions
Keywords
Deep learning, Non-cardiac surgery, Electrocardiogram, Multimodal, MACCE
Brief summary
This study aims to prospectively validate a deep learning model developed using retrospective data to predict major adverse cardiac and cerebrovascular events (MACCE) occurring within 30 days after non-cardiac surgery. The validation will be performed using prospectively collected data from patients undergoing non-cardiac surgery under general or regional anesthesia.
Interventions
None listed
Sponsors
Seoul National University Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Adults aged 18 or older undergoing non-cardiac surgery under general or regional anesthesia at Seoul National University Hospital
Exclusion criteria
* Reoperation within 30 days after initial surgery * Organ procurement or pregnancy-related procedures * Ambulatory-based surgery * Preoperative tracheal intubation before entering the operating room * Surgeries performed outside the operating room
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Major adverse cardiovascular and cerebrovascular events (MACCE) | within 30 days after surgery | Occurrence of MACCE within 30 days after surgery (composite outcome including myocardial infarction, stroke, coronary revascularization, heart failure, and death) |
Countries
South Korea
Contacts
Primary ContactHyun-Kyu Yoon, MD
Outcome results
None listed