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

NCT07539532 | Recruiting DMC

• 1 other identifier: BM_2025_238
• Study Type: observational
• Target: 5,000
• Locations: 1 country
• Sites: 1

Brief Summary

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

Keywords

Major adverse cardiovascular events; Gastrointestinal surgery; Artificial intelligence; Multimodal model

Outcome Measures

Primary Outcomes (1)

• 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.

  From the preoperative period to 30 days after surgery.

Secondary Outcomes (6)

• Brier score of the AI-based multimodal model for predicting 30-day major cardiovascular events after gastrointestinal surgery

  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

  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

  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

  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

  From the preoperative period to 30 days after surgery

Study Arms (1)

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.

Eligibility Criteria

• Age: 16 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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.

You may not qualify if:

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

Sponsors & Collaborators

• Bach Mai Hospital: lead

Study Sites (1)

Bach Mai hospital

Hà Nội, Vietnam

RECRUITING

Related Publications (11)

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  PMID: 38008807 RESULT

• Liu T, Krentz A, Lu L, Curcin V. Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis. Eur Heart J Digit Health. 2024 Oct 27;6(1):7-22. doi: 10.1093/ehjdh/ztae080. eCollection 2025 Jan.

  PMID: 39846062 RESULT

• Cheng CH, Lee BJ, Nfor ON, Hsiao CH, Huang YC, Liaw YP. Using machine learning-based algorithms to construct cardiovascular risk prediction models for Taiwanese adults based on traditional and novel risk factors. BMC Med Inform Decis Mak. 2024 Jul 22;24(1):199. doi: 10.1186/s12911-024-02603-2.

  PMID: 39039467 RESULT

• Li C, Liu X, Shen P, Sun Y, Zhou T, Chen W, Chen Q, Lin H, Tang X, Gao P. Improving cardiovascular risk prediction through machine learning modelling of irregularly repeated electronic health records. Eur Heart J Digit Health. 2023 Oct 17;5(1):30-40. doi: 10.1093/ehjdh/ztad058. eCollection 2024 Jan.

  PMID: 38264696 RESULT

• Kothari P, Vanneman MW, Choi C, Diehl R, Fielding-Singh V. Highlights from the American College of Cardiology and American Heart Association 2024 Guideline for Perioperative Cardiovascular Management for Noncardiac Surgery. J Cardiothorac Vasc Anesth. 2025 Sep;39(9):2408-2420. doi: 10.1053/j.jvca.2025.05.014. Epub 2025 May 14.

  PMID: 40480877 RESULT

• Writing Committee for the VISION Study Investigators; Devereaux PJ, Biccard BM, Sigamani A, Xavier D, Chan MTV, Srinathan SK, Walsh M, Abraham V, Pearse R, Wang CY, Sessler DI, Kurz A, Szczeklik W, Berwanger O, Villar JC, Malaga G, Garg AX, Chow CK, Ackland G, Patel A, Borges FK, Belley-Cote EP, Duceppe E, Spence J, Tandon V, Williams C, Sapsford RJ, Polanczyk CA, Tiboni M, Alonso-Coello P, Faruqui A, Heels-Ansdell D, Lamy A, Whitlock R, LeManach Y, Roshanov PS, McGillion M, Kavsak P, McQueen MJ, Thabane L, Rodseth RN, Buse GAL, Bhandari M, Garutti I, Jacka MJ, Schunemann HJ, Cortes OL, Coriat P, Dvirnik N, Botto F, Pettit S, Jaffe AS, Guyatt GH. Association of Postoperative High-Sensitivity Troponin Levels With Myocardial Injury and 30-Day Mortality Among Patients Undergoing Noncardiac Surgery. JAMA. 2017 Apr 25;317(16):1642-1651. doi: 10.1001/jama.2017.4360.

  PMID: 28444280 RESULT

• Vascular Events In Noncardiac Surgery Patients Cohort Evaluation (VISION) Study Investigators; Devereaux PJ, Chan MT, Alonso-Coello P, Walsh M, Berwanger O, Villar JC, Wang CY, Garutti RI, Jacka MJ, Sigamani A, Srinathan S, Biccard BM, Chow CK, Abraham V, Tiboni M, Pettit S, Szczeklik W, Lurati Buse G, Botto F, Guyatt G, Heels-Ansdell D, Sessler DI, Thorlund K, Garg AX, Mrkobrada M, Thomas S, Rodseth RN, Pearse RM, Thabane L, McQueen MJ, VanHelder T, Bhandari M, Bosch J, Kurz A, Polanczyk C, Malaga G, Nagele P, Le Manach Y, Leuwer M, Yusuf S. Association between postoperative troponin levels and 30-day mortality among patients undergoing noncardiac surgery. JAMA. 2012 Jun 6;307(21):2295-304. doi: 10.1001/jama.2012.5502.

  PMID: 22706835 RESULT

• Writing Committee Members; Thompson A, Fleischmann KE, Smilowitz NR, de Las Fuentes L, Mukherjee D, Aggarwal NR, Ahmad FS, Allen RB, Altin SE, Auerbach A, Berger JS, Chow B, Dakik HA, Eisenstein EL, Gerhard-Herman M, Ghadimi K, Kachulis B, Leclerc J, Lee CS, Macaulay TE, Mates G, Merli GJ, Parwani P, Poole JE, Rich MW, Ruetzler K, Stain SC, Sweitzer B, Talbot AW, Vallabhajosyula S, Whittle J, Williams KA Sr. 2024 AHA/ACC/ACS/ASNC/HRS/SCA/SCCT/SCMR/SVM Guideline for Perioperative Cardiovascular Management for Noncardiac Surgery: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol. 2024 Nov 5;84(19):1869-1969. doi: 10.1016/j.jacc.2024.06.013. Epub 2024 Sep 24.

  PMID: 39320289 RESULT

• Bilimoria KY, Liu Y, Paruch JL, Zhou L, Kmiecik TE, Ko CY, Cohen ME. Development and evaluation of the universal ACS NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and surgeons. J Am Coll Surg. 2013 Nov;217(5):833-42.e1-3. doi: 10.1016/j.jamcollsurg.2013.07.385. Epub 2013 Sep 18.

  PMID: 24055383 RESULT

• Gupta PK, Gupta H, Sundaram A, Kaushik M, Fang X, Miller WJ, Esterbrooks DJ, Hunter CB, Pipinos II, Johanning JM, Lynch TG, Forse RA, Mohiuddin SM, Mooss AN. Development and validation of a risk calculator for prediction of cardiac risk after surgery. Circulation. 2011 Jul 26;124(4):381-7. doi: 10.1161/CIRCULATIONAHA.110.015701. Epub 2011 Jul 5.

  PMID: 21730309 RESULT

• Lee TH, Marcantonio ER, Mangione CM, Thomas EJ, Polanczyk CA, Cook EF, Sugarbaker DJ, Donaldson MC, Poss R, Ho KK, Ludwig LE, Pedan A, Goldman L. Derivation and prospective validation of a simple index for prediction of cardiac risk of major noncardiac surgery. Circulation. 1999 Sep 7;100(10):1043-9. doi: 10.1161/01.cir.100.10.1043.

  PMID: 10477528 RESULT

MeSH Terms

Conditions

Postoperative Complications; Cardiovascular Diseases

Condition Hierarchy (Ancestors)

Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Target Duration: 30 Days
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator, Director, Center for Anesthesia and Surgical Intensive Care

Study Record Dates

• First Submitted: April 10, 2026
• First Posted: April 20, 2026
• Study Start: January 1, 2026
• Primary Completion (Estimated): June 30, 2026
• Study Completion (Estimated): July 31, 2026
• Last Updated: April 20, 2026

Record last verified: 2026-04

Data Sharing

• IPD Sharing: Will not share

Locations

VN (1)