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.
Adults aged 18 years or older.
Undergoing gastrointestinal surgery at Bach Mai Hospital between January 2025 and December 2026.
Available preoperative, intraoperative, and postoperative data sufficient for analysis.
Death within 24 hours after surgery due to a clearly non-cardiovascular cause.
Incomplete data required for analysis.
Adult patients undergoing gastrointestinal surgery at Bach Mai Hospital from January 2025 through December 2026. The study includes patients identified retrospectively from 2025 and prospectively from 2026 who have sufficient perioperative clinical data for analysis of 30-day major cardiovascular events after surgery.
Cohort
Other
Not listed
Area under the receiver operating characteristic curve of the AI-based multimodal model for predicting 30-day major adverse cardiovascular events after gastrointestinal surgery
Discrimination performance of the AI-based multimodal model for predicting 30-day major adverse cardiovascular events after gastrointestinal surgery.
Brier score of the AI-based multimodal model for predicting 30-day major cardiovascular events after gastrointestinal surgery
Overall prediction accuracy of the AI-based multimodal model as assessed by the Brier score. Lower values indicate better model performance.
Net Reclassification Improvement of the AI-Based Multimodal Model Compared With Traditional Risk Scores for Predicting 30-Day Major Cardiovascular Events After Gastrointestinal Surgery
Net reclassification improvement of the AI-based multimodal model compared with traditional risk scores for prediction of major cardiovascular events within 30 days after gastrointestinal surgery.
Integrated Discrimination Improvement of the AI-Based Multimodal Model Compared With Traditional Risk Scores for Predicting 30-Day Major Cardiovascular Events After Gastrointestinal Surgery
Integrated discrimination improvement of the AI-based multimodal model compared with traditional risk scores for prediction of major cardiovascular events within 30 days after gastrointestinal surgery.
Area under the receiver operating characteristic curve of the Revised Cardiac Risk Index for predicting 30-day major cardiovascular events after gastrointestinal surgery
Discrimination performance of the Revised Cardiac Risk Index.
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