Clinical Phenotype Classification of Atrial Fibrillation Patients Using Cluster Analysis and Associations with Trial-Adjudicated Outcomes

Overview

Journal Biomedicines

Specialty Biomedical Engineering

Date 2021 Aug 6

PMID 34356907

Citations 15

Authors

Marco Vitolo

Marco Proietti

Alena Shantsila

Giuseppe Boriani

Gregory Y H Lip

Affiliations

Soon will be listed here.

Abstract

Background And Purpose: Given the great clinical heterogeneity of atrial fibrillation (AF) patients, conventional classification only based on disease subtype or arrhythmia patterns may not adequately characterize this population. We aimed to identify different groups of AF patients who shared common clinical phenotypes using cluster analysis and evaluate the association between identified clusters and clinical outcomes.

Methods: We performed a hierarchical cluster analysis in AF patients from AMADEUS and BOREALIS trials. The primary outcome was a composite of stroke/thromboembolism (TE), cardiovascular (CV) death, myocardial infarction, and/or all-cause death. Individual components of the primary outcome and major bleeding were also assessed.

Results: We included 3980 AF patients treated with the Vitamin-K Antagonist from the AMADEUS and BOREALIS studies. The analysis identified four clusters in which patients varied significantly among clinical characteristics. Cluster 1 was characterized by patients with low rates of CV risk factors and comorbidities; Cluster 2 was characterized by patients with a high burden of CV risk factors; Cluster 3 consisted of patients with a high burden of CV comorbidities; Cluster 4 was characterized by the highest rates of non-CV comorbidities. After a mean follow-up of 365 (standard deviation 187) days, Cluster 4 had the highest cumulative risk of outcomes. Compared with Cluster 1, Cluster 4 was independently associated with an increased risk for the composite outcome (hazard ratio (HR) 2.43, 95% confidence interval (CI) 1.70-3.46), all-cause death (HR 2.35, 95% CI 1.58-3.49) and major bleeding (HR 2.18, 95% CI 1.19-3.96).

Conclusions: Cluster analysis identified four different clinically relevant phenotypes of AF patients that had unique clinical characteristics and different outcomes. Cluster analysis highlights the high degree of heterogeneity in patients with AF, suggesting the need for a phenotype-driven approach to comorbidities, which could provide a more holistic approach to management aimed to improve patients' outcomes.

Citing Articles

Outcomes in Atrial Fibrillation Patients with Different Clinical Phenotypes: Insights from the French Population.

Fawzy A, Bisson A, Lochon L, Lenormand T, Lip G, Fauchier L J Clin Med. 2025; 14(4).

PMID: 40004575 PMC: 11856015. DOI: 10.3390/jcm14041044.

Evaluation of the ABC pathway in patients with atrial fibrillation: A machine learning cluster analysis.

Wang J, Bian H, Tan J, Zhu J, Wang L, Xu W Int J Cardiol Heart Vasc. 2025; 57:101621.

PMID: 39995811 PMC: 11848476. DOI: 10.1016/j.ijcha.2025.101621.

Comorbidity patterns and mortality in atrial fibrillation: a latent class analysis of the EURopean study of Older Subjects with Atrial Fibrillation (EUROSAF).

Ng H, Woodman R, Veronese N, Pilotto A, Mangoni A Ann Med. 2025; 57(1):2454330.

PMID: 39825667 PMC: 11749148. DOI: 10.1080/07853890.2025.2454330.

Phenotypes of South Asian patients with atrial fibrillation and holistic integrated care management: cluster analysis of data from KERALA-AF Registry.

Chen Y, Huang B, Calvert P, Liu Y, Gue Y, Gupta D Lancet Reg Health Southeast Asia. 2024; 31:100507.

PMID: 39624353 PMC: 11609390. DOI: 10.1016/j.lansea.2024.100507.

The Application of Artificial Intelligence in Atrial Fibrillation Patients: From Detection to Treatment.

Liang H, Zhang H, Wang J, Shao X, Wu S, Lyu S Rev Cardiovasc Med. 2024; 25(7):257.

PMID: 39139434 PMC: 11317345. DOI: 10.31083/j.rcm2507257.

References

Falsetti L, Viticchi G, Buratti L, Grigioni F, Capucci A, Silvestrini M . Interactions between Atrial Fibrillation, Cardiovascular Risk Factors, and ApoE Genotype in Promoting Cognitive Decline in Patients with Alzheimer's Disease: A Prospective Cohort Study. J Alzheimers Dis. 2018; 62(2):713-725. DOI: 10.3233/JAD-170544. View

Sevakula R, Au-Yeung W, Singh J, Heist E, Isselbacher E, Armoundas A . State-of-the-Art Machine Learning Techniques Aiming to Improve Patient Outcomes Pertaining to the Cardiovascular System. J Am Heart Assoc. 2020; 9(4):e013924. PMC: 7070211. DOI: 10.1161/JAHA.119.013924. View

Parikh K, Rao Y, Ahmad T, Shen K, Felker G, Rajagopal S . Novel approach to classifying patients with pulmonary arterial hypertension using cluster analysis. Pulm Circ. 2017; 7(2):486-493. PMC: 5467940. DOI: 10.1177/2045893217705891. View

Horiuchi Y, Tanimoto S, Latif A, Urayama K, Aoki J, Yahagi K . Identifying novel phenotypes of acute heart failure using cluster analysis of clinical variables. Int J Cardiol. 2018; 262:57-63. DOI: 10.1016/j.ijcard.2018.03.098. View

Ahmad T, Pencina M, Schulte P, OBrien E, Whellan D, Pina I . Clinical implications of chronic heart failure phenotypes defined by cluster analysis. J Am Coll Cardiol. 2014; 64(17):1765-74. PMC: 4254424. DOI: 10.1016/j.jacc.2014.07.979. View

Vitolo M, Lip G, Shantsila A . Why Is Atrial Fibrillation So Frequent in Hypertensive Patients?. Am J Hypertens. 2020; 33(12):1067-1070. DOI: 10.1093/ajh/hpaa157. View

Inohara T, Piccini J, Mahaffey K, Kimura T, Katsumata Y, Tanimoto K . A Cluster Analysis of the Japanese Multicenter Outpatient Registry of Patients With Atrial Fibrillation. Am J Cardiol. 2019; 124(6):871-878. DOI: 10.1016/j.amjcard.2019.05.071. View

Guo Y, Lane D, Wang L, Zhang H, Wang H, Zhang W . Mobile Health Technology to Improve Care for Patients With Atrial Fibrillation. J Am Coll Cardiol. 2020; 75(13):1523-1534. DOI: 10.1016/j.jacc.2020.01.052. View

Malavasi V, Zoccali C, Brandi M, Micali G, Vitolo M, Imberti J . Cognitive impairment in patients with atrial fibrillation: Implications for outcome in a cohort study. Int J Cardiol. 2020; 323:83-89. DOI: 10.1016/j.ijcard.2020.08.028. View

10.

Inohara T, Shrader P, Pieper K, Blanco R, Thomas L, Singer D . Association of of Atrial Fibrillation Clinical Phenotypes With Treatment Patterns and Outcomes: A Multicenter Registry Study. JAMA Cardiol. 2017; 3(1):54-63. PMC: 5833527. DOI: 10.1001/jamacardio.2017.4665. View

11.

Boriani G, Vitolo M, Diemberger I, Proietti M, Valenti A, Malavasi V . Optimizing indices of atrial fibrillation susceptibility and burden to evaluate atrial fibrillation severity, risk and outcomes. Cardiovasc Res. 2021; 117(7):1-21. PMC: 8707734. DOI: 10.1093/cvr/cvab147. View

12.

Vitolo M, Proietti M, Harrison S, Lane D, Potpara T, Boriani G . The Euro Heart Survey and EURObservational Research Programme (EORP) in atrial fibrillation registries: contribution to epidemiology, clinical management and therapy of atrial fibrillation patients over the last 20 years. Intern Emerg Med. 2020; 15(7):1183-1192. DOI: 10.1007/s11739-020-02405-0. View

13.

Lip G, Banerjee A, Boriani G, Chiang C, Fargo R, Freedman B . Antithrombotic Therapy for Atrial Fibrillation: CHEST Guideline and Expert Panel Report. Chest. 2018; 154(5):1121-1201. DOI: 10.1016/j.chest.2018.07.040. View

14.

Shah S, Katz D, Selvaraj S, Burke M, Yancy C, Gheorghiade M . Phenomapping for novel classification of heart failure with preserved ejection fraction. Circulation. 2014; 131(3):269-79. PMC: 4302027. DOI: 10.1161/CIRCULATIONAHA.114.010637. View

15.

Ahmad T, Desai N, Wilson F, Schulte P, Dunning A, Jacoby D . Clinical Implications of Cluster Analysis-Based Classification of Acute Decompensated Heart Failure and Correlation with Bedside Hemodynamic Profiles. PLoS One. 2016; 11(2):e0145881. PMC: 4739604. DOI: 10.1371/journal.pone.0145881. View

16.

Lee S, Choi E, Jung J, Park S, Han K, Oh S . Body Mass Index and Clinical Outcomes in Asian Patients With Atrial Fibrillation Receiving Oral Anticoagulation. Stroke. 2021; 52(2):521-530. DOI: 10.1161/STROKEAHA.120.030356. View

17.

Viticchi G, Falsetti L, Burattini M, Zaccone V, Buratti L, Bartolini M . Atrial Fibrillation on Patients with Vascular Dementia: A Fundamental Target for Correct Management. Brain Sci. 2020; 10(7). PMC: 7407398. DOI: 10.3390/brainsci10070420. View

18.

Boriani G, Ruff C, Kuder J, Shi M, Lanz H, Antman E . Edoxaban versus Warfarin in Patients with Atrial Fibrillation at the Extremes of Body Weight: An Analysis from the ENGAGE AF-TIMI 48 Trial. Thromb Haemost. 2020; 121(2):140-149. DOI: 10.1055/s-0040-1716540. View

19.

Ernande L, Audureau E, Jellis C, Bergerot C, Henegar C, Sawaki D . Clinical Implications of Echocardiographic Phenotypes of Patients With Diabetes Mellitus. J Am Coll Cardiol. 2017; 70(14):1704-1716. DOI: 10.1016/j.jacc.2017.07.792. View

20.

Proietti M, Vitolo M, Lip G . Integrated care and outcomes in patients with atrial fibrillation and comorbidities. Eur J Clin Invest. 2021; 51(6):e13498. DOI: 10.1111/eci.13498. View