Artificial Intelligence Models to Predict Clinically Relevant Cardiovascular Outcomes

NCT ID: NCT06847100

Last Updated: 2025-08-26

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 273 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2023-02-06
• Study Completion Date: 2025-06-30

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.

Detailed Description

PerCard is a retrospective and prospective observational study. The study aims to develop and validate models for prediction of intrahospital AF and recurrence of coronary events in a long-term follow-up using Artificial Intelligence.

The development and internal validation of predictive models of AF involve two retrospective cohorts:

• Cohort A: 1258 patients underwent CABG at Centro Cardiologico Monzino (CCM) between 2002 and 2016
• Cohort B: 2445 patients admitted for AMI STEMI or NSTEMI to CCM between 2010 and 2018

The development and internal validation of predictive models of coronary event recurrence in long-term follow-up involve a third retrospective cohort:

-Cohort C: 1248 patients underwent CABG at CCM between 2002 and 2014 .

External validation of the predictive models of in-hospital AF involves a cohort of patients admitted with AMI STEMI or NSTEMI, who will be prospectively enrolled at Coronary Intensive Care Unit of Centro Cardiologico Monzino.

In the different prediction models, clinical and instrumental variables specific to patients with AMI (e.g., infarcted area), variables that are common to patients with any form of coronary revascularization (e.g., how many and which coronary vessels have been revascularized), or variables that are common to patients and individuals without established coronary artery disease (e.g., age, sex, history of hypertension, particular gene polymorphisms related to AF, signals from the ECG, etc.) will be included, where available.

In addition, the contribution of 16 gene polymorphisms associated with predisposition to intrahospital onset of AF has been previously evaluated in cohort A and will be evaluated and compared in the prospective cohort at the Immunology and Functional Genomics Research Unit of Centro Cardiologico Monzino.

Conditions

Atrial Fibrillation

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: OTHER

Study Groups

Prospective cohort

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

Blood withdrawal

Intervention Type: DIAGNOSTIC_TEST

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

Interventions

Blood withdrawal

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

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• 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)

Exclusion Criteria

• 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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Tampere University

OTHER

Sponsor Role: collaborator

Politecnico di Milano

OTHER

Sponsor Role: collaborator

Protestant University of Applied Sciences (Ludwigsburg, Germany)

UNKNOWN

Sponsor Role: collaborator

Centro Cardiologico Monzino

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Claudio Tondo, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

IRCCS Centro Cardiologico Monzino

Locations

Tampere University

Tampere, Pirkanmaa, Finland

Protestant University of Apllied Sciences Ludwigsburg

Ludwigsburg, Ludwigsburg, Germany

Politecnico di Milano

Milan, Milano, Italy

Centro Cardiologico Monzino

Milan, Milano, Italy

Countries

Finland; Germany; Italy

References

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.

Reference Type: BACKGROUND

PMID: 15364327 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 33951355 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 22107800 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 22359247 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 29447735 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 34777014 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 35639667 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 31123174 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 23574971 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 31791637 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 32833571 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 25560730 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 32397347 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 33624003 (View on PubMed)

Other Identifiers

CCM1860

Identifier Type: -

Identifier Source: org_study_id