A Discussion of the Contemporary Prediction Models for Atrial Fibrillation
Main Article Content
Abstract
Atrial Fibrillation is a complex disease state with many environmental and genetic risk factors. While there are environmental factors that have been shown to increase an individual’s risk of atrial fibrillation, it has become clear that atrial fibrillation has a genetic component that influences why some patients are at a higher risk of developing atrial fibrillation compared to others. This review will first discuss the clinical diagnosis of atrial fibrillation and the corresponding rhythm atrial flutter. We will then discuss how a patients’ risk of stroke can be assessed by using other clinical co-morbidities. We will then review the clinical risk factors that can be used to help predict an individual patient’s risk of atrial fibrillation. Many of the clinical risk factors have been used to create several different risk scoring methods that will be reviewed. We will then discuss how genetics can be used to identify individuals who are at higher risk for developing atrial fibrillation. We will discuss genome-wide association studies and other sequencing high-throughput sequencing studies. Finally, we will touch on how genetic variants derived from a genome-wide association studies can be used to calculate an individual’s polygenic risk score for atrial fibrillation. An atrial fibrillation polygenic risk score can be used to identify patients at higher risk of developing atrial fibrillation and may allow for a reduction in some of the complications associated with atrial fibrillation such as cerebrovascular accidents and the development of heart failure. Finally, there is a brief discussion of how artificial intelligence models can be used to predict which patients will develop atrial fibrillation.
Keywords:
Atrial Fibrillation, Clinical Risk Scores, Genome Wide Association Studies, Polygenic Risk Score
Article Details
How to Cite
ROSENBERG, Michael A.; ADEWUMI, Joseph; ALEONG, Ryan G..
A Discussion of the Contemporary Prediction Models for Atrial Fibrillation.
Medical Research Archives, [S.l.], v. 11, n. 10, oct. 2023.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/4481>. Date accessed: 21 nov. 2024.
doi: https://doi.org/10.18103/mra.v11i10.4481.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
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doi: 10.1161/JAHA.120.020194
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9. Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, Carter RE, Yao X, Rabinstein AA, Erickson BJ, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet. 2019. doi: 10.1016/s0140-6736(19)31721-0
10. Noseworthy PA, Attia ZI, Behnken EM, Giblon RE, Bews KA, Liu S, Gosse TA, Linn ZD, Deng Y, Yin J, et al. Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial. Lancet. 2022;400:1206-1212. doi: 10.1016/s0140-6736(22)01637-3
11. Rosenberg MA. Trusting Magic: Interpretability of Predictions From Machine Learning Algorithms. Circulation. 2021;143:1299-1301. doi: 10.1161/circulationaha.121.053733
12. Simon ST, Trinkley KE, Malone DC, Rosenberg MA. Interpretable Machine Learning Prediction of Drug-Induced QT Prolongation: Electronic Health Record Analysis. Journal of medical Internet research. 2022;24:e42163. doi: 10.2196/42163
13. Arnar DO, Thorvaldsson S, Manolio TA, Thorgeirsson G, Kristjansson K, Hakonarson H, Stefansson K. Familial aggregation of atrial fibrillation in Iceland. Eur Heart J. 2006;27:708-712.
doi: 10.1093/eurheartj/ehi727
14. Fox CS, Parise H, D'Agostino RB, Sr., Lloyd-Jones DM, Vasan RS, Wang TJ, Levy D, Wolf PA, Benjamin EJ. Parental atrial fibrillation as a risk factor for atrial fibrillation in offspring. JAMA. 2004;291:2851-2855.
doi: 10.1001/jama.291.23.2851
15. Lubitz SA, Yin X, Fontes JD, Magnani JW, Rienstra M, Pai M, Villalon ML, Vasan RS, Pencina MJ, Levy D, et al. Association between familial atrial fibrillation and risk of new-onset atrial fibrillation. JAMA. 2010;304:2263-2269. doi: 10.1001/jama.2010.1690
16. Gudbjartsson DF, Arnar DO, Helgadottir A, Gretarsdottir S, Holm H, Sigurdsson A, Jonasdottir A, Baker A, Thorleifsson G, Kristjansson K, et al. Variants conferring risk of atrial fibrillation on chromosome 4q25. Nature. 2007;448:353-357.
doi: 10.1038/nature06007
17. Huang Y, Wang C, Yao Y, Zuo X, Chen S, Xu C, Zhang H, Lu Q, Chang L, Wang F, et al. Molecular Basis of Gene-Gene Interaction: Cyclic Cross-Regulation of Gene Expression and Post-GWAS Gene-Gene Interaction Involved in Atrial Fibrillation. PLoS Genet. 2015;11:e1005393.
doi: 10.1371/journal.pgen.1005393
18. Mahida S, Ellinor PT. New advances in the genetic basis of atrial fibrillation. J Cardiovasc Electrophysiol. 2012;23:1400-1406. doi: 10.1111/j.1540-8167.2012.02445.x
19. Miyazawa K, Ito K, Ito M, Zou Z, Kubota M, Nomura S, Matsunaga H, Koyama S, Ieki H, Akiyama M, et al. Cross-ancestry genome-wide analysis of atrial fibrillation unveils disease biology and enables cardioembolic risk prediction. Nat Genet. 2023;55:187-197. doi: 10.1038/s41588-022-01284-9
20. Marston NA, Garfinkel AC, Kamanu FK, Melloni GM, Roselli C, Jarolim P, Berg DD, Bhatt DL, Bonaca MP, Cannon CP, et al. A polygenic risk score predicts atrial fibrillation in cardiovascular disease. Eur Heart J. 2023;44:221-231.
doi: 10.1093/eurheartj/ehac460
21. Al-Kaisey A, Wong GR, Young P, Chieng D, Hawson J, Anderson R, Sugumar H, Nalliah C, Prabhu M, Johnson R, et al. Polygenic risk scores are associated with atrial electrophysiologic substrate abnormalities and outcomes after atrial fibrillation catheter ablation. Heart Rhythm. 2023;20:1188-1194. doi: 10.1016/j.hrthm.2023.02.011
22. Conen D, Tedrow UB, Cook NR, Buring JE, Albert CM. Birth weight is a significant risk factor for incident atrial fibrillation. Circulation. 2010;122:764-770.
doi: CIRCULATIONAHA.110.947978 [pii] 10.1161/CIRCULATIONAHA.110.947978
23. Siddiqi HK, Vinayagamoorthy M, Gencer B, Ng C, Pester J, Cook NR, Lee IM, Buring J, Manson JE, Albert CM. Sex Differences in Atrial Fibrillation Risk: The VITAL Rhythm Study. JAMA cardiology. 2022;7:1027-1035.
doi: 10.1001/jamacardio.2022.2825
24. Liu CH, Lo LW, Chung FP, Chang SL, Hu YF, Lin YJ, Huang SC, Gan ST, Lin CY, Chao TF, et al. The impact of height on recurrence after index catheter ablation of paroxysmal atrial fibrillation. J Interv Card Electrophysiol. 2022;64:587-595. doi: 10.1007/s10840-021-01055-2
25. Rosenberg MA, Patton KK, Sotoodehnia N, Karas MG, Kizer JR, Zimetbaum PJ, Chang JD, Siscovick D, Gottdiener JS, Kronmal RA, et al. The impact of height on the risk of atrial fibrillation: the Cardiovascular Health Study. Eur Heart J. 2012;33:2709-2717. doi: 10.1093/eurheartj/ehs301
26. Roselli C, Chaffin MD, Weng LC, Aeschbacher S, Ahlberg G, Albert CM, Almgren P, Alonso A, Anderson CD, Aragam KG, et al. Multi-ethnic genome-wide association study for atrial fibrillation. Nat Genet. 2018. doi: 10.1038/s41588-018-0133-9
27. Rosenberg MA, Kaplan RC, Siscovick DS, Psaty BM, Heckbert SR, Newton-Cheh C, Mukamal KJ. Genetic variants related to height and risk of atrial fibrillation: the cardiovascular health study. Am J Epidemiol. 2014;180:215-222. doi: 10.1093/aje/kwu126
28. Levin MG, Judy R, Gill D, Vujkovic M, Verma SS, Bradford Y, Ritchie MD, Hyman MC, Nazarian S, Rader DJ, et al. Genetics of height and risk of atrial fibrillation: A Mendelian randomization study. PLoS medicine. 2020;17:e1003288.
doi: 10.1371/journal.pmed.1003288
29. Wang Q, Richardson TG, Sanderson E, Tudball MJ, Ala-Korpela M, Davey Smith G, Holmes MV. A phenome-wide bidirectional Mendelian randomization analysis of atrial fibrillation. Int J Epidemiol. 2022;51:1153-1166.
doi: 10.1093/ije/dyac041
doi: 10.1161/CIR.0000000000000665
2. Schnabel RB, Sullivan LM, Levy D, Pencina MJ, Massaro JM, D'Agostino RB, Sr., Newton-Cheh C, Yamamoto JF, Magnani JW, Tadros TM, et al. Development of a risk score for atrial fibrillation (Framingham Heart Study): a community-based cohort study. Lancet. 2009;373:739-745. doi: 10.1016/S0140-6736(09)60443-8
3. Alonso A, Krijthe BP, Aspelund T, Stepas KA, Pencina MJ, Moser CB, Sinner MF, Sotoodehnia N, Fontes JD, Janssens AC, et al. Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF consortium. Journal of the American Heart Association. 2013;2:e000102.
doi: 10.1161/JAHA.112.000102
4. Christophersen IE, Yin X, Larson MG, Lubitz SA, Magnani JW, McManus DD, Ellinor PT, Benjamin EJ. A comparison of the CHARGE-AF and the CHA2DS2-VASc risk scores for prediction of atrial fibrillation in the Framingham Heart Study. Am Heart J. 2016;178:45-54. doi: 10.1016/j.ahj.2016.05.004
5. Chao TF, Chiang CE, Chen TJ, Liao JN, Tuan TC, Chen SA. Clinical Risk Score for the Prediction of Incident Atrial Fibrillation: Derivation in 7 220 654 Taiwan Patients With 438 930 Incident Atrial Fibrillations During a 16-Year Follow-Up. J Am Heart Assoc. 2021;10:e020194.
doi: 10.1161/JAHA.120.020194
6. Tiwari P, Colborn KL, Smith DE, Xing F, Ghosh D, Rosenberg MA. Assessment of a Machine Learning Model Applied to Harmonized Electronic Health Record Data for the Prediction of Incident Atrial Fibrillation. JAMA Netw Open. 2020;3:e1919396-e1919396.
doi: 10.1001/jamanetworkopen.2019.19396
7. Ambale-Venkatesh B, Yang X, Wu CO, Liu K, Hundley WG, McClelland R, Gomes AS, Folsom AR, Shea S, Guallar E, et al. Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis. Circ Res. 2017;121:1092-1101.
doi: 10.1161/circresaha.117.311312
8. Raghunath S, Pfeifer JM, Ulloa-Cerna AE, Nemani A, Carbonati T, Jing L, vanMaanen DP, Hartzel DN, Ruhl JA, Lagerman BF, et al. Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke. Circulation. 2021;143:1287-1298.
doi: 10.1161/circulationaha.120.047829
9. Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, Carter RE, Yao X, Rabinstein AA, Erickson BJ, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet. 2019. doi: 10.1016/s0140-6736(19)31721-0
10. Noseworthy PA, Attia ZI, Behnken EM, Giblon RE, Bews KA, Liu S, Gosse TA, Linn ZD, Deng Y, Yin J, et al. Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial. Lancet. 2022;400:1206-1212. doi: 10.1016/s0140-6736(22)01637-3
11. Rosenberg MA. Trusting Magic: Interpretability of Predictions From Machine Learning Algorithms. Circulation. 2021;143:1299-1301. doi: 10.1161/circulationaha.121.053733
12. Simon ST, Trinkley KE, Malone DC, Rosenberg MA. Interpretable Machine Learning Prediction of Drug-Induced QT Prolongation: Electronic Health Record Analysis. Journal of medical Internet research. 2022;24:e42163. doi: 10.2196/42163
13. Arnar DO, Thorvaldsson S, Manolio TA, Thorgeirsson G, Kristjansson K, Hakonarson H, Stefansson K. Familial aggregation of atrial fibrillation in Iceland. Eur Heart J. 2006;27:708-712.
doi: 10.1093/eurheartj/ehi727
14. Fox CS, Parise H, D'Agostino RB, Sr., Lloyd-Jones DM, Vasan RS, Wang TJ, Levy D, Wolf PA, Benjamin EJ. Parental atrial fibrillation as a risk factor for atrial fibrillation in offspring. JAMA. 2004;291:2851-2855.
doi: 10.1001/jama.291.23.2851
15. Lubitz SA, Yin X, Fontes JD, Magnani JW, Rienstra M, Pai M, Villalon ML, Vasan RS, Pencina MJ, Levy D, et al. Association between familial atrial fibrillation and risk of new-onset atrial fibrillation. JAMA. 2010;304:2263-2269. doi: 10.1001/jama.2010.1690
16. Gudbjartsson DF, Arnar DO, Helgadottir A, Gretarsdottir S, Holm H, Sigurdsson A, Jonasdottir A, Baker A, Thorleifsson G, Kristjansson K, et al. Variants conferring risk of atrial fibrillation on chromosome 4q25. Nature. 2007;448:353-357.
doi: 10.1038/nature06007
17. Huang Y, Wang C, Yao Y, Zuo X, Chen S, Xu C, Zhang H, Lu Q, Chang L, Wang F, et al. Molecular Basis of Gene-Gene Interaction: Cyclic Cross-Regulation of Gene Expression and Post-GWAS Gene-Gene Interaction Involved in Atrial Fibrillation. PLoS Genet. 2015;11:e1005393.
doi: 10.1371/journal.pgen.1005393
18. Mahida S, Ellinor PT. New advances in the genetic basis of atrial fibrillation. J Cardiovasc Electrophysiol. 2012;23:1400-1406. doi: 10.1111/j.1540-8167.2012.02445.x
19. Miyazawa K, Ito K, Ito M, Zou Z, Kubota M, Nomura S, Matsunaga H, Koyama S, Ieki H, Akiyama M, et al. Cross-ancestry genome-wide analysis of atrial fibrillation unveils disease biology and enables cardioembolic risk prediction. Nat Genet. 2023;55:187-197. doi: 10.1038/s41588-022-01284-9
20. Marston NA, Garfinkel AC, Kamanu FK, Melloni GM, Roselli C, Jarolim P, Berg DD, Bhatt DL, Bonaca MP, Cannon CP, et al. A polygenic risk score predicts atrial fibrillation in cardiovascular disease. Eur Heart J. 2023;44:221-231.
doi: 10.1093/eurheartj/ehac460
21. Al-Kaisey A, Wong GR, Young P, Chieng D, Hawson J, Anderson R, Sugumar H, Nalliah C, Prabhu M, Johnson R, et al. Polygenic risk scores are associated with atrial electrophysiologic substrate abnormalities and outcomes after atrial fibrillation catheter ablation. Heart Rhythm. 2023;20:1188-1194. doi: 10.1016/j.hrthm.2023.02.011
22. Conen D, Tedrow UB, Cook NR, Buring JE, Albert CM. Birth weight is a significant risk factor for incident atrial fibrillation. Circulation. 2010;122:764-770.
doi: CIRCULATIONAHA.110.947978 [pii] 10.1161/CIRCULATIONAHA.110.947978
23. Siddiqi HK, Vinayagamoorthy M, Gencer B, Ng C, Pester J, Cook NR, Lee IM, Buring J, Manson JE, Albert CM. Sex Differences in Atrial Fibrillation Risk: The VITAL Rhythm Study. JAMA cardiology. 2022;7:1027-1035.
doi: 10.1001/jamacardio.2022.2825
24. Liu CH, Lo LW, Chung FP, Chang SL, Hu YF, Lin YJ, Huang SC, Gan ST, Lin CY, Chao TF, et al. The impact of height on recurrence after index catheter ablation of paroxysmal atrial fibrillation. J Interv Card Electrophysiol. 2022;64:587-595. doi: 10.1007/s10840-021-01055-2
25. Rosenberg MA, Patton KK, Sotoodehnia N, Karas MG, Kizer JR, Zimetbaum PJ, Chang JD, Siscovick D, Gottdiener JS, Kronmal RA, et al. The impact of height on the risk of atrial fibrillation: the Cardiovascular Health Study. Eur Heart J. 2012;33:2709-2717. doi: 10.1093/eurheartj/ehs301
26. Roselli C, Chaffin MD, Weng LC, Aeschbacher S, Ahlberg G, Albert CM, Almgren P, Alonso A, Anderson CD, Aragam KG, et al. Multi-ethnic genome-wide association study for atrial fibrillation. Nat Genet. 2018. doi: 10.1038/s41588-018-0133-9
27. Rosenberg MA, Kaplan RC, Siscovick DS, Psaty BM, Heckbert SR, Newton-Cheh C, Mukamal KJ. Genetic variants related to height and risk of atrial fibrillation: the cardiovascular health study. Am J Epidemiol. 2014;180:215-222. doi: 10.1093/aje/kwu126
28. Levin MG, Judy R, Gill D, Vujkovic M, Verma SS, Bradford Y, Ritchie MD, Hyman MC, Nazarian S, Rader DJ, et al. Genetics of height and risk of atrial fibrillation: A Mendelian randomization study. PLoS medicine. 2020;17:e1003288.
doi: 10.1371/journal.pmed.1003288
29. Wang Q, Richardson TG, Sanderson E, Tudball MJ, Ala-Korpela M, Davey Smith G, Holmes MV. A phenome-wide bidirectional Mendelian randomization analysis of atrial fibrillation. Int J Epidemiol. 2022;51:1153-1166.
doi: 10.1093/ije/dyac041