Postoperative ComplicationsGI-MACE-AINCT07539532
Comparing Traditional Risk Scores and an AI-Based Multimodal Model for Predicting Cardiovascular Events After Gastrointestinal Surgery
An observational study of Postoperative Complications and Cardiovascular Diseases, run by Bach Mai Hospital at 1 site in Vietnam.
Trial 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. Read more about this study
- Who is it for?
- Ages 16 years and older
- Conditions the study is for
- Postoperative Complications, Cardiovascular Diseases, Digestive System Surgical Procedures
- How many people
- 5,000 participants
- Where
- 1 siteVietnam
- Study dates
- 1 Jan 2026to 31 Jul 2026
- How long will I take part?
- 30 Days
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 16 years and older
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- Death within 24 hours after surgery due to a clearly non-cardiovascular cause.
- Incomplete data required for analysis.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Vietnam1 site, 1 recruiting
- Recruiting
Bach Mai hospital
Hà Nội
Nguyen Toan Thang, Thang, PhD, MD, nguyentoanthang@hmu.edu.vn
About this study
Official title: 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:
Read more
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.
Read the 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.
- Conditions
- Postoperative Complications, Cardiovascular Diseases, Digestive System Surgical Procedures
- Keywords
- Major adverse cardiovascular events, Gastrointestinal surgery, Artificial intelligence, Multimodal model
- Medical subject headings
- Postoperative Complications, Cardiovascular Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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
Overall prediction accuracy of the AI-based multimodal model as assessed by the Brier score. Lower values indicate better model performance.
Measured: 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
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.
Measured: 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
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.
Measured: 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
Discrimination performance of the Revised Cardiac Risk Index.
Measured: 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
Discrimination performance of the ACS NSQIP Surgical Risk Calculator.
Measured: 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
Agreement between predicted and observed risk as assessed by the calibration slope. A value closer to 1 indicates better calibration.
Measured: From the preoperative period to 30 days after surgery
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Apr 2026
- Study start1 Jan 2026
- Primary completion30 Jun 2026 (estimated)
- Study completion31 Jul 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (11)Papers the sponsor has linked to this study.
- Other trials for Postoperative ComplicationsStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 5,000 participants (estimated)
- Target duration
- 30 Days
Sponsor and investigators
- Lead sponsor
- Bach Mai Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Nguyen Toan Thang, Bach Mai Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07539532
- Sponsor's protocol ID
- BM_2025_238
- Acronym
- GI-MACE-AI
- Status last verified
- Apr 2026
- First submitted
- 10 Apr 2026
- Last updated
- 20 Apr 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: 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.
Publications
Gautam N, Mueller J, Alqaisi O, Gandhi T, Malkawi A, Tarun T, Alturkmani HJ, Zulqarnain MA, Pontone G, Al'Aref SJ. Machine Learning in Cardiovascular Risk Prediction and Precision Preventive Approaches. Curr Atheroscler Rep. 2023 Dec;25(12):1069-1081. doi: 10.1007/s11883-023-01174-3. Epub 2023 Nov 27.
PubMed 38008807Liu 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.
PubMed 39846062Cheng 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.
PubMed 39039467Li 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.
PubMed 38264696Kothari 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.
PubMed 40480877Writing 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.
PubMed 28444280Vascular 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.
PubMed 22706835Writing 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.
PubMed 39320289Bilimoria 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.
PubMed 24055383Gupta 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.
PubMed 21730309Lee 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.
PubMed 10477528
Study at a glance
From the registry record, last updated by the sponsor on 20 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Value of Some Risk Scores in Predicting Cardiovascular Events After Gastrointestinal Surgery
- ClinicalTrials.gov ID
- NCT07539532
- Acronym / sponsor ID
- GI-MACE-AI / BM_2025_238
- Conditions
- Postoperative Complications, Cardiovascular Diseases, Digestive System Surgical Procedures
- Keywords
- Major adverse cardiovascular events, Gastrointestinal surgery, Artificial intelligence, Multimodal model
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 5,000 participants (estimated)
- Lead sponsor
- Bach Mai HospitalUniversity, hospital or other
- Study start
- 1 Jan 2026 (actual)
- Primary completion
- 30 Jun 2026 (estimated)
- Study completion
- 31 Jul 2026 (estimated)
- First posted
- 20 Apr 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Cohort
- Time perspective
- Other
- Target follow-up
- 30 Days
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 16 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (2 listed)
- Death within 24 hours after surgery due to a clearly non-cardiovascular cause.
- Incomplete data required for analysis.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
6 secondary outcomes
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
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
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
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
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
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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Apr 2026.
- Results publication: PubMed 38008807Gautam N, Mueller J, Alqaisi O, Gandhi T, Malkawi A, Tarun T, Alturkmani HJ, Zulqarnain MA, Pontone G, Al'Aref SJ. Machine Learning in Cardiovascular Risk Prediction and Precision Preventive Approaches. Curr Atheroscler Rep. 2023 Dec;25(12):1069-1081. doi: 10.1007/s11883-023-01174-3. Epub 2023 Nov 27.
- Results publication: PubMed 39846062Liu 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.
- Results publication: PubMed 39039467Cheng 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.
- Results publication: PubMed 38264696Li 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.
- Results publication: PubMed 40480877Kothari 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.
- Results publication: PubMed 28444280Writing 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.
- Results publication: PubMed 22706835Vascular 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.
- Results publication: PubMed 39320289Writing 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.
- Results publication: PubMed 24055383Bilimoria 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.
- Results publication: PubMed 21730309Gupta 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.
- Results publication: PubMed 10477528Lee 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.
- Individual participant dataSharing planned: No. 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Postoperative Complications and Cardiovascular Diseases, run by Bach Mai Hospital at 1 site in Vietnam.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 20 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Vietnam. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07539532, which also lists 11 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 Apr 2026.