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

April 16, 2026 updated by: Nguyen Toan Thang, Bach Mai Hospital

Value of Some Risk Scores in Predicting Cardiovascular Events After Gastrointestinal Surgery

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.

Study Overview

Status

Recruiting

Conditions

Detailed Description

Major cardiovascular events after gastrointestinal surgery remain an important cause of early postoperative complications and poor outcomes. Traditional perioperative cardiac risk scores, including 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), are widely used in clinical practice. However, their performance may be limited in specific surgical populations and may not fully capture complex interactions among clinical, laboratory, physiologic, and procedural variables.

This observational study aims to develop and evaluate an artificial intelligence (AI)-based multimodal model for predicting major cardiovascular events within 30 days after gastrointestinal surgery and to compare its predictive performance with traditional risk scores. The study will be conducted at Bach Mai Hospital and will include adult patients undergoing gastrointestinal surgery. The study uses a mixed retrospective-prospective design, with retrospective data collection from patients treated in 2025 and prospective data collection in 2026.

The target clinical outcome for prediction is the occurrence of major cardiovascular events within 30 days after surgery. These events include cardiovascular death, nonfatal myocardial infarction, cardiac arrest with return of spontaneous circulation, new stroke, and clinically significant arrhythmias requiring treatment. Data used for model development and comparison may include demographic characteristics, medical history, cardiovascular comorbidities, surgical characteristics, anesthetic information, preoperative laboratory results, electrocardiographic findings, biomarkers when available, and functional or risk assessment variables.

The primary outcome of the study is the discrimination performance of the AI-based multimodal model compared with traditional risk scores, measured by the area under the receiver operating characteristic curve for predicting 30-day major cardiovascular events after gastrointestinal surgery. Secondary outcomes include calibration performance, net reclassification improvement, and integrated discrimination improvement of the AI-based multimodal model compared with traditional risk scores, including RCRI, ACS NSQIP MICA, and ACS NSQIP SRC.

The study is observational and will not alter routine perioperative management. Data will be obtained from existing medical records and prospective clinical collection, coded for confidentiality, and analyzed to support risk stratification and model comparison in patients undergoing gastrointestinal surgery.

Study Type

Observational

Enrollment (Estimated)

5000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

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.

Description

Inclusion Criteria:

  • 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.

Exclusion Criteria:

  • Death within 24 hours after surgery due to a clearly non-cardiovascular cause.
  • Incomplete data required for analysis.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Overall Study Cohort
Adults undergoing gastrointestinal surgery at Bach Mai Hospital who are included in this observational study and followed for major cardiovascular events within 30 days after surgery. The study includes retrospective data from 2025 and prospective data from 2026.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Area under the receiver operating characteristic curve of the AI-based multimodal model for predicting 30-day major adverse cardiovascular events after gastrointestinal surgery
Time Frame: From the preoperative period to 30 days after surgery.
Discrimination performance of the AI-based multimodal model for predicting 30-day major adverse cardiovascular events after gastrointestinal surgery.
From the preoperative period to 30 days after surgery.

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Brier score of the AI-based multimodal model for predicting 30-day major cardiovascular events after gastrointestinal surgery
Time Frame: From the preoperative period to 30 days after surgery
Overall prediction accuracy of the AI-based multimodal model as assessed by the Brier score. Lower values indicate better model performance.
From the preoperative period to 30 days after surgery
Net Reclassification Improvement of the AI-Based Multimodal Model Compared With Traditional Risk Scores for Predicting 30-Day Major Cardiovascular Events After Gastrointestinal Surgery
Time Frame: Using perioperative data collected from the preoperative period through 30 days after 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.
Using perioperative data collected from the preoperative period through 30 days after 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
Time Frame: Using perioperative data collected from the preoperative period through 30 days after 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.
Using perioperative data collected from the preoperative period through 30 days after surgery
Area under the receiver operating characteristic curve of the Revised Cardiac Risk Index for predicting 30-day major cardiovascular events after gastrointestinal surgery
Time Frame: From the preoperative period to 30 days after surgery
Discrimination performance of the Revised Cardiac Risk Index.
From the preoperative period to 30 days after surgery
Area under the receiver operating characteristic curve of the ACS NSQIP Surgical Risk Calculator for predicting 30-day major cardiovascular events after gastrointestinal surgery
Time Frame: From the preoperative period to 30 days after surgery
Discrimination performance of the ACS NSQIP Surgical Risk Calculator.
From the preoperative period to 30 days after surgery
Calibration slope of the AI-based multimodal model for predicting 30-day major cardiovascular events after gastrointestinal surgery
Time Frame: From the preoperative period to 30 days after surgery
Agreement between predicted and observed risk as assessed by the calibration slope. A value closer to 1 indicates better calibration.
From the preoperative period to 30 days after surgery

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Bach Mai Hospital

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 1, 2026

Primary Completion (Estimated)

June 30, 2026

Study Completion (Estimated)

July 31, 2026

Study Registration Dates

First Submitted

April 10, 2026

First Submitted That Met QC Criteria

April 16, 2026

First Posted (Actual)

April 20, 2026

Study Record Updates

Last Update Posted (Actual)

April 20, 2026

Last Update Submitted That Met QC Criteria

April 16, 2026

Last Verified

April 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • BM_2025_238

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

Individual participant data will not be shared because the study uses hospital-based clinical data containing potentially identifiable information, and no formal external data-sharing plan has been approved.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

Clinical Trials on Cardiovascular Diseases

Search Similar Trials

Sponsors and Collaborators

Medical Conditions

Drug Interventions

CROs by country

CROs in Cameroon

Conditions

Rare Diseases

Drug Interventions

Dietary Supplements

Sponsor/Collaborators

Locations

3
Subscribe