Atrial FibrillationPERCARDNCT06847100
Artificial Intelligence Models to Predict Clinically Relevant Cardiovascular Outcomes
An observational study of Atrial Fibrillation, run by Centro Cardiologico Monzino at 4 sites in 3 countries.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Atrial Fibrillation
- What is being tested?
- Blood withdrawal
- How many people
- 273 participants
- Where
- 4 sitesin 3 countries
- Study dates
- 6 Feb 2023to 30 Jun 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: 500 patients admitted to the Coronary Intensive Care Unit of the CCM for AMI (STEMI or NSTEMI)
Who may be able to join
- 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)
Who cannot join
- 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
Trial locations and contacts
4 sites in 3 countries.
4 sites in 3 countries
Italy2 sites
Politecnico di Milano
Milan, Milano, 20133
Centro Cardiologico Monzino
Milan, Milano, 20138
Germany1 site
Protestant University of Apllied Sciences Ludwigsburg
Ludwigsburg, Ludwigsburg, 71638
About this study
Official title: Development of Artificial Intelligence Models to Predict Intrahospital Atrial Fibrillation and Long-term Coronary Event Recurrence in High-risk Patients: PerCard Study
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.
Read the 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
- Being tested
- Blood withdrawal
- Keywords
- Acute Myocardial Infarction (STEMI, NSTEMI), Artificial Intelligence, Genetics
- Medical subject headings
- Atrial Fibrillation, ST Elevation Myocardial Infarction, Non-ST Elevated Myocardial Infarction
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Blood withdrawalDiagnostic test
Optional collection of 5 mL of blood to assess the contribution of 16 gene polymorphisms AF-associated
| Study group | Role | What they receive |
|---|---|---|
| Prospective cohort | Patients who will be admitted for AMI (STEMI or NSTEMI) at Intensive Care Unit of Centro Cardiologico Monzino Receives: Diagnostic Test: Blood withdrawal |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 1 year
Secondary outcomes (1)
Genetic evaluation of polymorphisms associated with Atrial Fibrillation
Evaluation and comparison of the contribution of 16 AF predisposition-associated gene polymorphisms on the intrahospital onset of arrhythmia between patients who underwent CABG and patients with AMI.
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Feb 2025
- Study start6 Feb 2023
- Primary completion30 Jun 2025
- Study completion30 Jun 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (14)Papers the sponsor has linked to this study.
- Other trials for Atrial FibrillationStudies 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
- 273 participants (actual)
Sponsor and investigators
- Lead sponsor
- Centro Cardiologico Monzino
- Sponsor type
- University, hospital or other
- Collaborators
- Tampere University, Politecnico di Milano, Protestant University of Applied Sciences (Ludwigsburg, Germany)
- Responsible party
- Sponsor
- Investigators
- Claudio Tondo, MD, PhD, Principal investigator, IRCCS Centro Cardiologico Monzino
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06847100
- Sponsor's protocol ID
- CCM1860
- Acronym
- PERCARD
- Status last verified
- Aug 2025
- First submitted
- 21 Feb 2025
- Last updated
- 26 Aug 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 15364327Louka 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.
PubMed 33951355Mrdovic 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.
PubMed 22107800Beukema 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.
PubMed 22359247Kosmidou 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.
PubMed 29447735Tseng 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.
PubMed 34777014van 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.
PubMed 35639667Huang 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.
PubMed 31123174Dorresteijn 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.
PubMed 23574971Santos 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.
PubMed 31791637Siontis 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.
PubMed 32833571Moons 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.
PubMed 25560730Cosentino 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.
PubMed 32397347Werba 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.
PubMed 33624003
Study at a glance
From the registry record, last updated by the sponsor on 26 Aug 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of Artificial Intelligence Models to Predict Intrahospital Atrial Fibrillation and Long-term Coronary Event Recurrence in High-risk Patients: PerCard Study
- ClinicalTrials.gov ID
- NCT06847100
- Acronym / sponsor ID
- PERCARD / CCM1860
- Conditions
- Atrial Fibrillation
- Keywords
- Acute Myocardial Infarction (STEMI, NSTEMI), Artificial Intelligence, Genetics
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 273 participants (actual)
- Lead sponsor
- Centro Cardiologico MonzinoUniversity, hospital or other
- Collaborators
- Tampere University, Politecnico di Milano, Protestant University of Applied Sciences (Ludwigsburg, Germany)
- Study start
- 6 Feb 2023 (actual)
- Primary completion
- 30 Jun 2025 (actual)
- Study completion
- 30 Jun 2025 (actual)
- First posted
- 26 Feb 2025 (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.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Prospective cohort | – | Diagnostic Test: Blood withdrawalPatients who will be admitted for AMI (STEMI or NSTEMI) at Intensive Care Unit of Centro Cardiologico Monzino |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- 500 patients admitted to the Coronary Intensive Care Unit of the CCM for AMI (STEMI or NSTEMI)
Key inclusion criteria (3 listed)
- 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)
Key exclusion criteria (2 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Blood withdrawalOptional collection of 5 mL of blood to assess the contribution of 16 gene polymorphisms AF-associated | Diagnostic test | Prospective cohort |
Primary outcome
Validation of the intrahospital AF prediction model in the prospective cohort
Time frame: 1 year
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 secondary outcome
Genetic evaluation of polymorphisms associated with Atrial Fibrillation
Time frame: 1 year
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Aug 2025.
- Background: PubMed 15364327Amar 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.
- Background: PubMed 33951355Louka 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.
- Background: PubMed 22107800Mrdovic 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.
- Background: PubMed 22359247Beukema 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.
- Background: PubMed 29447735Kosmidou 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.
- Background: PubMed 34777014Tseng 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.
- Background: PubMed 35639667van 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.
- Background: PubMed 31123174Huang 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.
- Background: PubMed 23574971Dorresteijn 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.
- Background: PubMed 31791637Santos 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.
- Background: PubMed 32833571Siontis 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.
- Background: PubMed 25560730Moons 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.
- Individual participant dataSharing planned: No
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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. This study is not recruiting at the moment.
Claudio Tondo, MD, PhD principal investigator
IRCCS Centro Cardiologico Monzino
4 sites in 3 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Atrial Fibrillation, run by Centro Cardiologico Monzino at 4 sites in 3 countries.
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: finished, as of the sponsor's last update on 26 Aug 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 4 sites in Italy, Finland, Germany. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Validation of the intrahospital AF prediction model in the prospective cohort (time frame: 1 year). 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 NCT06847100, which also lists 14 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 26 Aug 2025; AzTrials last refreshed its copy on 29 Oct 2026.