Warning Model of Myocardial Remodeling After Acute Myocardial Infarction Using Multimodal Feature Structure Technology

NCT ID: NCT06062316

Last Updated: 2024-04-30

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 4000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-10-10
• Study Completion Date: 2024-06-01

Brief Summary

Acute myocardial infarction (AMI) is one of the most important diseases threatening human life. The existing MI prognosis prediction scales mostly predict the incidence of death, recurrent MI and heart failure through 6-8 clinical text indicators, and the data are collected relatively simply. Myocardial remodeling, as an adverse pathological change that can start and continue to progress in the early stage after myocardial infarction, is the main pathological mechanism of heart failure and death. However, there is no quantitative early-warning model of myocardial remodeling, and the clinical guidance of early intervention is lacking.

Our previous study found that cardiac magnetic resonance imaging can accurately quantify the necrotic area and recoverable myocardium in the edematous myocardium after myocardial infarction. In this study, machine learning algorithm, variable convolution network (DCN) and capsule network (capsnet) are used to build a new neural network architecture. Structural feature extraction of multi-modal clinical image data such as MRI and ultrasound is realized. Combined with the established database of 3000 patients with myocardial infarction, the multimodal feature matrix will be constructed, and a variety of classifiers such as support vector machine (SVM) and random forest (RF) will be used for quantitative prediction of myocardial remodeling, and the effects of different classifiers were evaluated. It is expected that this project will establish a quantitative early warning model of myocardial remodeling after acute myocardial infarction in line with the characteristics of Chinese people. The same type of data outside the database will be used for verification to establish an efficient and stable early warning model.

Conditions

Myocardial Infarction

Study Design

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

Eligibility Criteria

Inclusion Criteria

• 1.Patients with acute myocardial infarction, aged 18-80 years; 2.The time from onset to treatment is less than 72h 3.Myocardial enzyme Tni/Tnt(+).

Exclusion Criteria

• 1.Patients with malignant tumors; 2.Patients who could not receive conventional treatment 3.Patients who did not receive coronary angiography, lacking anatomical and imaging data; 4.Patients who have undergone cardiac surgery (except coronary artery bypass surgery)

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 80 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Beijing Institute of Technology

OTHER

Sponsor Role: collaborator

Xuanwu Hospital, Beijing

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Zhi Liu

Role: PRINCIPAL_INVESTIGATOR

Xuanwu Hospital, Beijing

Locations

Xuanwu Hospital, Capital Medical University

Beijing, Xicheng, China

Countries

China

References

Moran AE, Forouzanfar MH, Roth GA, Mensah GA, Ezzati M, Murray CJ, Naghavi M. Temporal trends in ischemic heart disease mortality in 21 world regions, 1980 to 2010: the Global Burden of Disease 2010 study. Circulation. 2014 Apr 8;129(14):1483-92. doi: 10.1161/CIRCULATIONAHA.113.004042. Epub 2014 Feb 26.

Reference Type: BACKGROUND

PMID: 24573352 (View on PubMed)

Abuomara HZA, Hassan OM, Rashid T, Baraka M. Myocardial performance index as an echocardiographic predictor of early in-hospital heart failure during first acute anterior ST-elevation myocardial infarction. Egypt Heart J. 2018 Jun;70(2):71-75. doi: 10.1016/j.ehj.2017.12.001. Epub 2017 Dec 24.

Reference Type: BACKGROUND

PMID: 30166885 (View on PubMed)

Hausenloy DJ, Yellon DM. Myocardial ischemia-reperfusion injury: a neglected therapeutic target. J Clin Invest. 2013 Jan;123(1):92-100. doi: 10.1172/JCI62874. Epub 2013 Jan 2.

Reference Type: BACKGROUND

PMID: 23281415 (View on PubMed)

Hendriks T, Schurer RAJ, Al Ali L, van den Heuvel AFM, van der Harst P. Left ventricular restoration devices post myocardial infarction. Heart Fail Rev. 2018 Nov;23(6):871-883. doi: 10.1007/s10741-018-9711-2.

Reference Type: BACKGROUND

PMID: 29770903 (View on PubMed)

Iborra-Egea O, Galvez-Monton C, Roura S, Perea-Gil I, Prat-Vidal C, Soler-Botija C, Bayes-Genis A. Mechanisms of action of sacubitril/valsartan on cardiac remodeling: a systems biology approach. NPJ Syst Biol Appl. 2017 Apr 18;3:12. doi: 10.1038/s41540-017-0013-4. eCollection 2017.

Reference Type: BACKGROUND

PMID: 28649439 (View on PubMed)

McMurray JJ, Packer M, Desai AS, Gong J, Lefkowitz MP, Rizkala AR, Rouleau JL, Shi VC, Solomon SD, Swedberg K, Zile MR; PARADIGM-HF Investigators and Committees. Angiotensin-neprilysin inhibition versus enalapril in heart failure. N Engl J Med. 2014 Sep 11;371(11):993-1004. doi: 10.1056/NEJMoa1409077. Epub 2014 Aug 30.

Reference Type: BACKGROUND

PMID: 25176015 (View on PubMed)

Seferovic PM, Fragasso G, Petrie M, Mullens W, Ferrari R, Thum T, Bauersachs J, Anker SD, Ray R, Cavusoglu Y, Polovina M, Metra M, Ambrosio G, Prasad K, Seferovic J, Jhund PS, Dattilo G, Celutkiene J, Piepoli M, Moura B, Chioncel O, Ben Gal T, Heymans S, Jaarsma T, Hill L, Lopatin Y, Lyon AR, Ponikowski P, Lainscak M, Jankowska E, Mueller C, Cosentino F, Lund LH, Filippatos GS, Ruschitzka F, Coats AJS, Rosano GMC. Heart Failure Association of the European Society of Cardiology update on sodium-glucose co-transporter 2 inhibitors in heart failure. Eur J Heart Fail. 2020 Nov;22(11):1984-1986. doi: 10.1002/ejhf.2026. Epub 2020 Oct 27.

Reference Type: BACKGROUND

PMID: 33068051 (View on PubMed)

Pasternak B, Ueda P, Eliasson B, Svensson AM, Franzen S, Gudbjornsdottir S, Hveem K, Jonasson C, Wintzell V, Melbye M, Svanstrom H. Use of sodium glucose cotransporter 2 inhibitors and risk of major cardiovascular events and heart failure: Scandinavian register based cohort study. BMJ. 2019 Aug 29;366:l4772. doi: 10.1136/bmj.l4772.

Reference Type: BACKGROUND

PMID: 31467044 (View on PubMed)

West R, Jones D. Cardiac rehabilitation and mortality reduction after myocardial infarction: the emperor's new clothes? Evidence against cardiac rehabilitation. Heart. 2013 Jul;99(13):911-3. doi: 10.1136/heartjnl-2013-303705. Epub 2013 May 4.

Reference Type: BACKGROUND

PMID: 23644409 (View on PubMed)

Anderson L, Oldridge N, Thompson DR, Zwisler AD, Rees K, Martin N, Taylor RS. Exercise-Based Cardiac Rehabilitation for Coronary Heart Disease: Cochrane Systematic Review and Meta-Analysis. J Am Coll Cardiol. 2016 Jan 5;67(1):1-12. doi: 10.1016/j.jacc.2015.10.044.

Reference Type: BACKGROUND

PMID: 26764059 (View on PubMed)

Milani RV, Lavie CJ. Impact of cardiac rehabilitation on depression and its associated mortality. Am J Med. 2007 Sep;120(9):799-806. doi: 10.1016/j.amjmed.2007.03.026.

Reference Type: BACKGROUND

PMID: 17765050 (View on PubMed)

Karuzas A, Rumbinaite E, Verikas D, Ptasinskas T, Muckiene G, Kazakauskaite E, Zabiela V, Jurkevicius R, Vaskelyte JJ, Zaliunas R, Zaliaduonyte-Peksiene D. Accuracy of three-dimensional systolic dyssynchrony and sphericity indexes for identifying early left ventricular remodeling after acute myocardial infarction. Anatol J Cardiol. 2019 Jun;22(1):13-20. doi: 10.14744/AnatolJCardiol.2019.02844.

Reference Type: BACKGROUND

PMID: 31264652 (View on PubMed)

Kagiyama N, Shrestha S, Cho JS, Khalil M, Singh Y, Challa A, Casaclang-Verzosa G, Sengupta PP. A low-cost texture-based pipeline for predicting myocardial tissue remodeling and fibrosis using cardiac ultrasound. EBioMedicine. 2020 Apr;54:102726. doi: 10.1016/j.ebiom.2020.102726. Epub 2020 Apr 6.

Reference Type: BACKGROUND

PMID: 32268274 (View on PubMed)

Chirinos JA, Sardana M, Ansari B, Satija V, Kuriakose D, Edelstein I, Oldland G, Miller R, Gaddam S, Lee J, Suri A, Akers SR. Left Atrial Phasic Function by Cardiac Magnetic Resonance Feature Tracking Is a Strong Predictor of Incident Cardiovascular Events. Circ Cardiovasc Imaging. 2018 Dec;11(12):e007512. doi: 10.1161/CIRCIMAGING.117.007512.

Reference Type: BACKGROUND

PMID: 30562112 (View on PubMed)

Ranka S, Reddy M, Noheria A. Artificial intelligence in cardiovascular medicine. Curr Opin Cardiol. 2021 Jan;36(1):26-35. doi: 10.1097/HCO.0000000000000812.

Reference Type: BACKGROUND

PMID: 33060388 (View on PubMed)

Ahmad T, Lund LH, Rao P, Ghosh R, Warier P, Vaccaro B, Dahlstrom U, O'Connor CM, Felker GM, Desai NR. Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients. J Am Heart Assoc. 2018 Apr 12;7(8):e008081. doi: 10.1161/JAHA.117.008081.

Reference Type: BACKGROUND

PMID: 29650709 (View on PubMed)

Motwani M, Dey D, Berman DS, Germano G, Achenbach S, Al-Mallah MH, Andreini D, Budoff MJ, Cademartiri F, Callister TQ, Chang HJ, Chinnaiyan K, Chow BJ, Cury RC, Delago A, Gomez M, Gransar H, Hadamitzky M, Hausleiter J, Hindoyan N, Feuchtner G, Kaufmann PA, Kim YJ, Leipsic J, Lin FY, Maffei E, Marques H, Pontone G, Raff G, Rubinshtein R, Shaw LJ, Stehli J, Villines TC, Dunning A, Min JK, Slomka PJ. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. Eur Heart J. 2017 Feb 14;38(7):500-507. doi: 10.1093/eurheartj/ehw188.

Reference Type: BACKGROUND

PMID: 27252451 (View on PubMed)

Tokodi M, Schwertner WR, Kovacs A, Toser Z, Staub L, Sarkany A, Lakatos BK, Behon A, Boros AM, Perge P, Kutyifa V, Szeplaki G, Geller L, Merkely B, Kosztin A. Machine learning-based mortality prediction of patients undergoing cardiac resynchronization therapy: the SEMMELWEIS-CRT score. Eur Heart J. 2020 May 7;41(18):1747-1756. doi: 10.1093/eurheartj/ehz902.

Reference Type: BACKGROUND

PMID: 31923316 (View on PubMed)

Sanchez-Cabo F, Rossello X, Fuster V, Benito F, Manzano JP, Silla JC, Fernandez-Alvira JM, Oliva B, Fernandez-Friera L, Lopez-Melgar B, Mendiguren JM, Sanz J, Ordovas JM, Andres V, Fernandez-Ortiz A, Bueno H, Ibanez B, Garcia-Ruiz JM, Lara-Pezzi E. Machine Learning Improves Cardiovascular Risk Definition for Young, Asymptomatic Individuals. J Am Coll Cardiol. 2020 Oct 6;76(14):1674-1685. doi: 10.1016/j.jacc.2020.08.017.

Reference Type: BACKGROUND

PMID: 33004133 (View on PubMed)

Commandeur F, Slomka PJ, Goeller M, Chen X, Cadet S, Razipour A, McElhinney P, Gransar H, Cantu S, Miller RJH, Rozanski A, Achenbach S, Tamarappoo BK, Berman DS, Dey D. Machine learning to predict the long-term risk of myocardial infarction and cardiac death based on clinical risk, coronary calcium, and epicardial adipose tissue: a prospective study. Cardiovasc Res. 2020 Dec 1;116(14):2216-2225. doi: 10.1093/cvr/cvz321.

Reference Type: BACKGROUND

PMID: 31853543 (View on PubMed)

Than MP, Pickering JW, Sandoval Y, Shah ASV, Tsanas A, Apple FS, Blankenberg S, Cullen L, Mueller C, Neumann JT, Twerenbold R, Westermann D, Beshiri A, Mills NL; MI3 Collaborative. Machine Learning to Predict the Likelihood of Acute Myocardial Infarction. Circulation. 2019 Sep 10;140(11):899-909. doi: 10.1161/CIRCULATIONAHA.119.041980. Epub 2019 Aug 16.

Reference Type: BACKGROUND

PMID: 31416346 (View on PubMed)

Tan JH, Hagiwara Y, Pang W, Lim I, Oh SL, Adam M, Tan RS, Chen M, Acharya UR. Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals. Comput Biol Med. 2018 Mar 1;94:19-26. doi: 10.1016/j.compbiomed.2017.12.023. Epub 2018 Jan 2.

Reference Type: BACKGROUND

PMID: 29358103 (View on PubMed)

D'Ascenzo F, De Filippo O, Gallone G, Mittone G, Deriu MA, Iannaccone M, Ariza-Sole A, Liebetrau C, Manzano-Fernandez S, Quadri G, Kinnaird T, Campo G, Simao Henriques JP, Hughes JM, Dominguez-Rodriguez A, Aldinucci M, Morbiducci U, Patti G, Raposeiras-Roubin S, Abu-Assi E, De Ferrari GM; PRAISE study group. Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets. Lancet. 2021 Jan 16;397(10270):199-207. doi: 10.1016/S0140-6736(20)32519-8.

Reference Type: BACKGROUND

PMID: 33453782 (View on PubMed)

Fernandez-Ruiz I. Machine learning predicts risk in ACS. Nat Rev Cardiol. 2021 Apr;18(4):230. doi: 10.1038/s41569-021-00521-8. No abstract available.

Reference Type: BACKGROUND

PMID: 33531699 (View on PubMed)

Wang S, Zhao Y, Zhao Y, Wang Y, Fan Z, Liu Z. Construction and Clinical Relevance of a Predictive Model of Coronary Microcirculatory Dysfunction in Patients With Acute Myocardial Infarction Following Percutaneous Coronary Intervention. Rev Cardiovasc Med. 2025 Jun 30;26(6):38533. doi: 10.31083/RCM38533. eCollection 2025 Jun.

Reference Type: DERIVED

PMID: 40630444 (View on PubMed)

Other Identifiers

xuanwuliuzhi

Identifier Type: -

Identifier Source: org_study_id