Artificial Intelligence Models to Predict Clinically Relevant Cardiovascular Outcomes

NCT06847100 | Completed

• 1 other identifier: CCM1860
• Study Type: observational
• Target: 273
• Locations: 3 countries
• Sites: 4

Brief Summary

Atrial fibrillation (AF) is a frequent and clinically relevant problem among the events that may occur during the hospitalization period in patients with cardiovascular disease. AF, indeed, is a determinant or aggravating condition of serious adverse events, such as myocardial infarction, heart failure, and thromboembolic stroke. The occurrence of AF in hospitalized patients, such as those admitted for coronary intervention, results in prolonged length of hospitalization, increased likelihood of discharge on anticoagulants, and increased 30-day risk of bleeding. It is noteworthy that while the incidence of AF in the general population is about 1-2 cases per 1000 people per year, this is much higher in patients hospitalized for acute myocardial infarction (AMI) (about 10% over the hospitalization period) or in patients undergoing coronary artery bypass grafting (CABG) (about 25% over the hospitalization period). Thus, identifying patients at high risk of AF during the hospitalization period could allow experimental testing of the efficacy and safety of preventive interventions (e.g., tailored anesthetic or surgical approaches, drug-prevention, etc.). It can be hypothesized that the clinical and nonclinical variables useful in estimating the risk of AF will change depending on the type of patients and that the identification and integration of these variables will require more complex predictive analysis systems than the regression models classically used to develop risk scores. On the other hand, the risk of recurrence of coronary events throughout the first years after CABG remains high (about 20% at 5 years) despite effective revascularization and early secondary prevention.Although some scores have been developed for estimating the risk of coronary event recurrence in secondary prevention using multivariate regression models, these algorithms consider a limited number of predictors, do not take into account possible interactions between different factors, and their actual predictive ability is not reported in the literature. With advances in Artificial Intelligence (AI) technology together with the rapid development of digital clinical datasets, machine learning has the potential to analyze substantial amounts of data and recognize patterns to predict AF onset and recurrence of coronary events within a defined time horizon (e.g., in-hospital event) in selected populations in a way that improves the predictive ability of conventional methods.

Conditions

Atrial Fibrillation

Keywords

Acute Myocardial Infarction (STEMI, NSTEMI); Artificial Intelligence; Genetics

Outcome Measures

Primary Outcomes (1)

• Validation of the intrahospital AF prediction model in the prospective cohort

  External validation ("narrow external validation") of the intrahospital AF prediction model in a cohort of patients who will be admitted for AMI (STEMI or NSTEMI) at the CCM.

  1 year

Secondary Outcomes (1)

• Genetic evaluation of polymorphisms associated with Atrial Fibrillation

  1 year

Study Arms (1)

Prospective cohort

Patients who will be admitted for AMI (STEMI or NSTEMI) at Intensive Care Unit of Centro Cardiologico Monzino

Diagnostic Test: Blood withdrawal

Interventions

Blood withdrawal DIAGNOSTIC_TEST

Optional collection of 5 mL of blood to assess the contribution of 16 gene polymorphisms AF-associated

Prospective cohort

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

500 patients admitted to the Coronary Intensive Care Unit of the CCM for AMI (STEMI or NSTEMI)

You may qualify if:

• age ≥18 years
• patient admitted to the Coronary Intensive Care Unit of the CCM for AMI (STEMI or NSTEMI)
• signature of informed consent to use clinical and instrumental data and, optionally, genetic data specific to the purpose of this study (gene polymorphisms presumably related to the development of AF)

You may not qualify if:

• any chronic or acute condition that prevents the patient from consciously consenting to the use of his or her personal, clinical, and instrumental data
• patients already in acute or permanent AF at the time of admission

Sponsors & Collaborators

• Centro Cardiologico Monzino: lead
• Tampere University: collaborator
• Politecnico di Milano: collaborator
• Protestant University of Applied Sciences (Ludwigsburg, Germany): collaborator

Study Sites (4)

Tampere University

Tampere, Pirkanmaa, 33100, Finland

Location

Protestant University of Apllied Sciences Ludwigsburg

Ludwigsburg, Ludwigsburg, 71638, Germany

Location

Politecnico di Milano

Milan, Milano, 20133, Italy

Location

Centro Cardiologico Monzino

Milan, Milano, 20138, Italy

Location

Related Publications (14)

• Amar D, Shi W, Hogue CW Jr, Zhang H, Passman RS, Thomas B, Bach PB, Damiano R, Thaler HT. Clinical prediction rule for atrial fibrillation after coronary artery bypass grafting. J Am Coll Cardiol. 2004 Sep 15;44(6):1248-53. doi: 10.1016/j.jacc.2004.05.078.

  PMID: 15364327 BACKGROUND

• Louka AM, Tsagkaris C, Stoica A. Clinical risk scores for the prediction of incident atrial fibrillation: a modernized review. Rom J Intern Med. 2021 Nov 20;59(4):321-327. doi: 10.2478/rjim-2021-0018. Print 2021 Dec 1.

  PMID: 33951355 BACKGROUND

• Mrdovic I, Savic L, Krljanac G, Perunicic J, Asanin M, Lasica R, Antonijevic N, Kocev N, Marinkovic J, Vasiljevic Z, Ostojic M. Incidence, predictors, and 30-day outcomes of new-onset atrial fibrillation after primary percutaneous coronary intervention: insight into the RISK-PCI trial. Coron Artery Dis. 2012 Jan;23(1):1-8. doi: 10.1097/MCA.0b013e32834df552.

  PMID: 22107800 BACKGROUND

• Beukema RJ, Elvan A, Ottervanger JP, de Boer MJ, Hoorntje JC, Suryapranata H, Dambrink JH, Gosselink AT, van 't Hof AW; Zwolle Myocardial Infarction Study Group. Atrial fibrillation after but not before primary angioplasty for ST-segment elevation myocardial infarction of prognostic importance. Neth Heart J. 2012 Apr;20(4):155-60. doi: 10.1007/s12471-012-0242-5.

  PMID: 22359247 BACKGROUND

• Kosmidou I, Chen S, Kappetein AP, Serruys PW, Gersh BJ, Puskas JD, Kandzari DE, Taggart DP, Morice MC, Buszman PE, Bochenek A, Schampaert E, Page P, Sabik JF 3rd, McAndrew T, Redfors B, Ben-Yehuda O, Stone GW. New-Onset Atrial Fibrillation After PCI or CABG for Left Main Disease: The EXCEL Trial. J Am Coll Cardiol. 2018 Feb 20;71(7):739-748. doi: 10.1016/j.jacc.2017.12.012.

  PMID: 29447735 BACKGROUND

• Tseng AS, Noseworthy PA. Prediction of Atrial Fibrillation Using Machine Learning: A Review. Front Physiol. 2021 Oct 28;12:752317. doi: 10.3389/fphys.2021.752317. eCollection 2021.

  PMID: 34777014 BACKGROUND

• van Smeden M, Heinze G, Van Calster B, Asselbergs FW, Vardas PE, Bruining N, de Jaegere P, Moore JH, Denaxas S, Boulesteix AL, Moons KGM. Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease. Eur Heart J. 2022 Aug 14;43(31):2921-2930. doi: 10.1093/eurheartj/ehac238.

  PMID: 35639667 BACKGROUND

• Huang D, Cheng YY, Wong YT, Yung SY, Chan KW, Lam CC, Hai J, Lau CP, Wong KL, Feng YQ, Tan N, Chen JY, Wu MX, Su X, Yan H, Song D, Tse HF, Chan PH, Siu CW, Tam CC. TIMI risk score for secondary prevention of recurrent cardiovascular events in a real-world cohort of post-non-ST-elevation myocardial infarction patients. Postgrad Med J. 2019 Jul;95(1125):372-377. doi: 10.1136/postgradmedj-2019-136404. Epub 2019 May 23.

  PMID: 31123174 BACKGROUND

• Dorresteijn JA, Visseren FL, Wassink AM, Gondrie MJ, Steyerberg EW, Ridker PM, Cook NR, van der Graaf Y; SMART Study Group. Development and validation of a prediction rule for recurrent vascular events based on a cohort study of patients with arterial disease: the SMART risk score. Heart. 2013 Jun;99(12):866-72. doi: 10.1136/heartjnl-2013-303640. Epub 2013 Apr 10.

  PMID: 23574971 BACKGROUND

• Santos ASAC, Rodrigues APS, Rosa LPS, Sarrafzadegan N, Silveira EA. Cardiometabolic risk factors and Framingham Risk Score in severely obese patients: Baseline data from DieTBra trial. Nutr Metab Cardiovasc Dis. 2020 Mar 9;30(3):474-482. doi: 10.1016/j.numecd.2019.10.010. Epub 2019 Nov 5.

  PMID: 31791637 BACKGROUND

• Siontis KC, Yao X, Pirruccello JP, Philippakis AA, Noseworthy PA. How Will Machine Learning Inform the Clinical Care of Atrial Fibrillation? Circ Res. 2020 Jun 19;127(1):155-169. doi: 10.1161/CIRCRESAHA.120.316401. Epub 2020 Jun 18.

  PMID: 32833571 BACKGROUND

• Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, Vickers AJ, Ransohoff DF, Collins GS. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015 Jan 6;162(1):W1-73. doi: 10.7326/M14-0698.

  PMID: 25560730 BACKGROUND

• Cosentino N, Ballarotto M, Campodonico J, Milazzo V, Bonomi A, Genovesi S, Moltrasio M, De Metrio M, Rubino M, Veglia F, Assanelli E, Marana I, Grazi M, Lauri G, Bartorelli AL, Marenzi G. Impact of Glomerular Filtration Rate on the Incidence and Prognosis of New-Onset Atrial Fibrillation in Acute Myocardial Infarction. J Clin Med. 2020 May 9;9(5):1396. doi: 10.3390/jcm9051396.

  PMID: 32397347 BACKGROUND

• Werba JP, Bonomi A, Giroli M, Amato M, Vigo L, Agrifoglio M, Alamanni F, Cavallotti L, Kassem S, Naliato M, Parolari A, Penza E, Polvani G, Pompilio G, Porqueddu M, Roberto M, Salis S, Zanobini M, Amato M, Baldassarre D, Veglia F, Tremoli E. Long-term secondary cardiovascular prevention programme in patients subjected to coronary artery bypass surgery. Eur J Prev Cardiol. 2022 May 25;29(7):997-1004. doi: 10.1093/eurjpc/zwaa060.

  PMID: 33624003 BACKGROUND

Biospecimen

Retention: SAMPLES WITH DNA

genetic analysis

MeSH Terms

Conditions

Atrial Fibrillation; ST Elevation Myocardial Infarction; Non-ST Elevated Myocardial Infarction

Condition Hierarchy (Ancestors)

Arrhythmias, Cardiac; Heart Diseases; Cardiovascular Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Myocardial Infarction; Myocardial Ischemia; Vascular Diseases; Infarction; Ischemia; Necrosis

Study Officials

• Claudio Tondo, MD, PhD

  IRCCS Centro Cardiologico Monzino

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 21, 2025
• First Posted: February 26, 2025
• Study Start: February 6, 2023
• Primary Completion: June 30, 2025
• Study Completion: June 30, 2025
• Last Updated: August 26, 2025

Record last verified: 2025-08

Data Sharing

• IPD Sharing: Will not share

Locations

FI (1); DE (1); IT (2)