Comparing Traditional Risk Scores and an AI-Based Multimodal Model for Predicting Cardiovascular Events After Gastrointestinal Surgery

Recruiting now

Conditions studied: Postoperative Complications, Cardiovascular Diseases, Digestive System Surgical Procedures

In brief

The goal of this observational study is to develop and evaluate an artificial intelligence (AI)-based multimodal model for predicting major cardiovascular events within 30 days after gastrointestinal surgery in adults at Bach Mai Hospital. The study will also compare the predictive performance of this AI-based model with commonly used traditional risk scores. The main questions it aims to answer are: Can an AI-based multimodal model predict major cardiovascular events within 30 days after gastrointestinal surgery? Does the AI-based model show better predictive performance than the Revised Cardiac Risk Index (RCRI), the American College of Surgeons National Surgical Quality Improvement Program Myocardial Infarction or Cardiac Arrest calculator (ACS NSQIP MICA), and the ACS NSQIP Surgical Risk Calculator (ACS NSQIP SRC)? Researchers will compare the AI-based multimodal model with traditional risk scores using measures of predictive performance, including discrimination, calibration, net reclassification improvement, and integrated discrimination improvement. Participants will be adults undergoing gastrointestinal surgery. Researchers will review medical record data from patients treated in 2025 and will also collect the same types of clinical data prospectively in 2026. The clinical outcome being predicted is the occurrence of major cardiovascular events within 30 days after surgery. The study will not change routine clinical care.

Key facts

Study ID
NCT07539532
Run by
Bach Mai Hospital
People needed
5000
Starts
2026-01-01
Expected to finish
2026-07-31
Last updated by the study team
2026-04-20

Who can join

Age: 16 and older. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

You may not qualify if…

Where it is running

Full record on ClinicalTrials.gov

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