Current and Future Approach in Premature Coronary Artery Disease
DOI:
https://doi.org/10.11594/jk-risk.05.1.6Keywords:
Premature Coronary Artery Disease, Multimodality Approach, Artificial Intelligence, Machine LearningAbstract
Coronary artery disease is one of the most frequent cardiovascular diseases and has consistently ranked as the leading cause of death globally for the past three decades. During that period, trends regarding coronary artery disease in young adults have exhibited a complicated interaction of rising incidence and mortality rates in particular populations. Coronary artery disease encompasses a broad range of symptoms and is particularly concerning when it develops at a young age, typically before 55 years in men and 60 years in women. The disease spectrum varied from asymptomatic conditions to chronic symptoms known as chronic coronary syndrome and acute symptoms known as acute coronary syndrome. Multiple recommendations and consensus have been established concerning the approaches to these spectrums, although there is less discourse on precisely addressing the young population's specific requirements. This extensive analysis offers insights into premature coronary artery disease and the multidimensional approach based on the latest guidelines and the novel approach using the help of artificial intelligence and machine learning, emphasizing early detection and diagnosis across the whole clinical spectrum.
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References
1. Institute for Health Metrics and Evaluation (IHME). Global Burden of Disease 2021: Findings from the GBD 2021 Study. May 2024. Accessed October 30, 2024. https://www.healthdata.org/research-analysis/library/global-burden-disease-2021-findings-gbd-2021-study
2. Martin SS, Aday AW, Almarzooq ZI, et al. 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. Circulation. 2024;149(8):e347-e913. doi:10.1161/CIR.0000000000001209
3. Visseren FLJ, Mach F, Smulders YM, et al. 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice: Developed by the Task Force for cardiovascular disease prevention in clinical practice with representatives of the European Society of Cardiology and 12 medical societies With the special contribution of the European Association of Preventive Cardiology (EAPC). Eur Heart J. 2021;42(34):3227-3337. doi:10.1093/eurheartj/ehab484
4. Michos ED, Choi AD. Coronary Artery Disease in Young Adults. J Am Coll Cardiol. 2019;74(15):1879-1882. doi:10.1016/j.jacc.2019.08.1023
5. Vrints C, Andreotti F, Koskinas KC, et al. 2024 ESC Guidelines for the management of chronic coronary syndromes: Developed by the task force for the management of chronic coronary syndromes of the European Society of Cardiology (ESC) Endorsed by the European Association for Cardio-Thoracic Surgery (EACTS). Eur Heart J. 2024;45(36):3415-3537. doi:10.1093/eurheartj/ehae177
6. Smith CL, Seigerman M, Adusumalli S, et al. Evolution and Outcomes of Premature Coronary Artery Disease. Curr Cardiol Rep. 2021;23(4):36. doi:10.1007/s11886-021-01457-8
7. Abohelwa M, Kopel J, Shurmur S, Ansari MM, Awasthi Y, Awasthi S. The Framingham Study on Cardiovascular Disease Risk and Stress-Defenses: A Historical Review. J Vasc Dis. 2023;2(1):122-164. doi:10.3390/jvd2010010
8. O’Donnell CJ, Elosua R. Cardiovascular Risk Factors. Insights From Framingham Heart Study. Rev Esp Cardiol Engl Ed. 2008;61(3):299-310. doi:10.1016/S1885-5857(08)60118-8
9. Virani SS, Newby LK, Arnold SV, et al. 2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease. J Am Coll Cardiol. 2023;82(9):833-955. doi:10.1016/j.jacc.2023.04.003
10. Wang H, Liu Z, Shao J, et al. Pathogenesis of premature coronary artery disease: Focus on risk factors and genetic variants. Genes Dis. 2022;9(2):370-380. doi:10.1016/j.gendis.2020.11.003
11. Noverike N, Rahimah AF, Rohman MS. The Complexity of Premature Coronary Artery Disease. Heart Sci J. 2024;5(3):4-9. doi:10.21776/ub/hsj.2024.005.03.2
12. Byrne RA, Rossello X, Coughlan JJ, et al. 2023 ESC Guidelines for the management of acute coronary syndromes. Eur Heart J. 2023;44(38):3720-3826. doi:10.1093/eurheartj/ehad191
13. Winchester DE, Maron DJ, Blankstein R, et al. ACC/AHA/ASE/ASNC/ASPC/HFSA/HRS/SCAI/SCCT/SCMR/STS 2023 Multimodality Appropriate Use Criteria for the Detection and Risk Assessment of Chronic Coronary Disease. J Am Coll Cardiol. 2023;81(25):2445-2467. doi:10.1016/j.jacc.2023.03.410
14. Collet JP, Zeitouni M, Procopi N, et al. Long-Term Evolution of Premature Coronary Artery Disease. J Am Coll Cardiol. 2019;74(15):1868-1878. doi:10.1016/j.jacc.2019.08.1002
15. Loria CM, Liu K, Lewis CE, et al. Early Adult Risk Factor Levels and Subsequent Coronary Artery Calcification: The CARDIA Study. J Am Coll Cardiol. 2007;49(20):2013-2020. doi:10.1016/j.jacc.2007.03.009
16. Lester SJ, Eleid MF, Khandheria BK, Hurst RT. Carotid Intima-Media Thickness and Coronary Artery Calcium Score as Indications of Subclinical Atherosclerosis. Mayo Clin Proc. 2009;84(3):229-233. doi:10.4065/84.3.229
17. Arnett DK, Blumenthal RS, Albert MA, et al. 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2019;140(11):e563-e595. doi:10.1161/CIR.0000000000000677
18. Kolli KK, Park SH, Min JK, et al. Machine learning algorithm to predict coronary artery calcification in asymptomatic healthy population. In: 2019 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2019. Institute of Electrical and Electronics Engineers Inc.; 2019:95-98. doi:10.1109/HI-POCT45284.2019.8962647
19. Kolossváry M, Cecco CND, Feuchtner G, Maurovich-Horvat P. Advanced atherosclerosis imaging by CT: Radiomics, machine learning and deep learning. J Cardiovasc Comput Tomogr. 2019;13(5):274-280. doi:10.1016/j.jcct.2019.04.007
20. Cao J, Zhang L, Ma L, Zhou X, Yang B, Wang W. Study on the risk of coronary heart disease in middle-aged and young people based on machine learning methods: a retrospective cohort study. PeerJ. 2022;10(1):01-15. doi:10.7717/peerj.14078
21. Juan-Salvadores P, Veiga C, Jiménez Díaz VA, et al. Using Machine Learning Techniques to Predict MACE in Very Young Acute Coronary Syndrome Patients. Diagnostics. 2022;12(2):422. doi:10.3390/diagnostics12020422
22. Akella A, Akella S. Machine Learning Algorithms for Predicting Coronary Artery Disease: Efforts Toward an Open Source Solution. Future Sci OA. 2021;7(6):01-10. doi:10.2144/fsoa-2020-0206
2. Martin SS, Aday AW, Almarzooq ZI, et al. 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. Circulation. 2024;149(8):e347-e913. doi:10.1161/CIR.0000000000001209
3. Visseren FLJ, Mach F, Smulders YM, et al. 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice: Developed by the Task Force for cardiovascular disease prevention in clinical practice with representatives of the European Society of Cardiology and 12 medical societies With the special contribution of the European Association of Preventive Cardiology (EAPC). Eur Heart J. 2021;42(34):3227-3337. doi:10.1093/eurheartj/ehab484
4. Michos ED, Choi AD. Coronary Artery Disease in Young Adults. J Am Coll Cardiol. 2019;74(15):1879-1882. doi:10.1016/j.jacc.2019.08.1023
5. Vrints C, Andreotti F, Koskinas KC, et al. 2024 ESC Guidelines for the management of chronic coronary syndromes: Developed by the task force for the management of chronic coronary syndromes of the European Society of Cardiology (ESC) Endorsed by the European Association for Cardio-Thoracic Surgery (EACTS). Eur Heart J. 2024;45(36):3415-3537. doi:10.1093/eurheartj/ehae177
6. Smith CL, Seigerman M, Adusumalli S, et al. Evolution and Outcomes of Premature Coronary Artery Disease. Curr Cardiol Rep. 2021;23(4):36. doi:10.1007/s11886-021-01457-8
7. Abohelwa M, Kopel J, Shurmur S, Ansari MM, Awasthi Y, Awasthi S. The Framingham Study on Cardiovascular Disease Risk and Stress-Defenses: A Historical Review. J Vasc Dis. 2023;2(1):122-164. doi:10.3390/jvd2010010
8. O’Donnell CJ, Elosua R. Cardiovascular Risk Factors. Insights From Framingham Heart Study. Rev Esp Cardiol Engl Ed. 2008;61(3):299-310. doi:10.1016/S1885-5857(08)60118-8
9. Virani SS, Newby LK, Arnold SV, et al. 2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease. J Am Coll Cardiol. 2023;82(9):833-955. doi:10.1016/j.jacc.2023.04.003
10. Wang H, Liu Z, Shao J, et al. Pathogenesis of premature coronary artery disease: Focus on risk factors and genetic variants. Genes Dis. 2022;9(2):370-380. doi:10.1016/j.gendis.2020.11.003
11. Noverike N, Rahimah AF, Rohman MS. The Complexity of Premature Coronary Artery Disease. Heart Sci J. 2024;5(3):4-9. doi:10.21776/ub/hsj.2024.005.03.2
12. Byrne RA, Rossello X, Coughlan JJ, et al. 2023 ESC Guidelines for the management of acute coronary syndromes. Eur Heart J. 2023;44(38):3720-3826. doi:10.1093/eurheartj/ehad191
13. Winchester DE, Maron DJ, Blankstein R, et al. ACC/AHA/ASE/ASNC/ASPC/HFSA/HRS/SCAI/SCCT/SCMR/STS 2023 Multimodality Appropriate Use Criteria for the Detection and Risk Assessment of Chronic Coronary Disease. J Am Coll Cardiol. 2023;81(25):2445-2467. doi:10.1016/j.jacc.2023.03.410
14. Collet JP, Zeitouni M, Procopi N, et al. Long-Term Evolution of Premature Coronary Artery Disease. J Am Coll Cardiol. 2019;74(15):1868-1878. doi:10.1016/j.jacc.2019.08.1002
15. Loria CM, Liu K, Lewis CE, et al. Early Adult Risk Factor Levels and Subsequent Coronary Artery Calcification: The CARDIA Study. J Am Coll Cardiol. 2007;49(20):2013-2020. doi:10.1016/j.jacc.2007.03.009
16. Lester SJ, Eleid MF, Khandheria BK, Hurst RT. Carotid Intima-Media Thickness and Coronary Artery Calcium Score as Indications of Subclinical Atherosclerosis. Mayo Clin Proc. 2009;84(3):229-233. doi:10.4065/84.3.229
17. Arnett DK, Blumenthal RS, Albert MA, et al. 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2019;140(11):e563-e595. doi:10.1161/CIR.0000000000000677
18. Kolli KK, Park SH, Min JK, et al. Machine learning algorithm to predict coronary artery calcification in asymptomatic healthy population. In: 2019 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2019. Institute of Electrical and Electronics Engineers Inc.; 2019:95-98. doi:10.1109/HI-POCT45284.2019.8962647
19. Kolossváry M, Cecco CND, Feuchtner G, Maurovich-Horvat P. Advanced atherosclerosis imaging by CT: Radiomics, machine learning and deep learning. J Cardiovasc Comput Tomogr. 2019;13(5):274-280. doi:10.1016/j.jcct.2019.04.007
20. Cao J, Zhang L, Ma L, Zhou X, Yang B, Wang W. Study on the risk of coronary heart disease in middle-aged and young people based on machine learning methods: a retrospective cohort study. PeerJ. 2022;10(1):01-15. doi:10.7717/peerj.14078
21. Juan-Salvadores P, Veiga C, Jiménez Díaz VA, et al. Using Machine Learning Techniques to Predict MACE in Very Young Acute Coronary Syndrome Patients. Diagnostics. 2022;12(2):422. doi:10.3390/diagnostics12020422
22. Akella A, Akella S. Machine Learning Algorithms for Predicting Coronary Artery Disease: Efforts Toward an Open Source Solution. Future Sci OA. 2021;7(6):01-10. doi:10.2144/fsoa-2020-0206
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2025-10-31
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How to Cite
Firdaus, A. J., & Satrijo, B. (2025). Current and Future Approach in Premature Coronary Artery Disease. Jurnal Klinik Dan Riset Kesehatan, 5(1), 49-62. https://doi.org/10.11594/jk-risk.05.1.6
