AI-Based Multimodal Model for Predicting Post-GI Surgery Cardiovascular Events vs Traditional Risk Scores
Summary
NIH registered an observational study (NCT07539532) at Bach Mai Hospital comparing an AI-based multimodal model against traditional cardiac risk scores (RCRI, ACS NSQIP MICA, and ACS NSQIP SRC) for predicting major cardiovascular events within 30 days after gastrointestinal surgery in adults. The study will use retrospective 2025 medical records and prospective 2026 data collection. No changes to routine clinical care are involved.
What changed
This study registration documents an observational research project at Bach Mai Hospital evaluating whether an AI-based multimodal model outperforms established cardiac risk indices in predicting postoperative cardiovascular events. The study compares the AI model against three traditional tools: the Revised Cardiac Risk Index, the ACS NSQIP MICA calculator, and the ACS NSQIP Surgical Risk Calculator. Researchers will assess discrimination, calibration, net reclassification improvement, and integrated discrimination improvement.
Healthcare institutions and researchers developing or evaluating AI-based clinical prediction tools should note this study's methodology for head-to-head comparison against established risk frameworks. Academic medical centers and surgical quality improvement programs monitoring AI adoption in perioperative risk assessment may find this trial's outcome measures — including net reclassification improvement and integrated discrimination improvement — relevant to their own evaluation frameworks.
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Apr 21, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
Comparing Traditional Risk Scores and an AI-Based Multimodal Model for Predicting Cardiovascular Events After Gastrointestinal Surgery
Observational NCT07539532 Kind: OBSERVATIONAL Apr 20, 2026
Abstract
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.
Conditions: Postoperative Complications, Cardiovascular Diseases, Digestive System Surgical Procedures
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