Clinical Phenotype Classification of Atrial Fibrillation Patients Using Cluster Analysis and Associations with Trial-Adjudicated Outcomes.
atrial fibrillation
cluster analysis
phenotype classification
stroke
Journal
Biomedicines
ISSN: 2227-9059
Titre abrégé: Biomedicines
Pays: Switzerland
ID NLM: 101691304
Informations de publication
Date de publication:
20 Jul 2021
20 Jul 2021
Historique:
received:
08
06
2021
revised:
10
07
2021
accepted:
15
07
2021
entrez:
6
8
2021
pubmed:
7
8
2021
medline:
7
8
2021
Statut:
epublish
Résumé
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. 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. 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). 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.
Sections du résumé
BACKGROUND AND PURPOSE
OBJECTIVE
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
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
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
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.
Identifiants
pubmed: 34356907
pii: biomedicines9070843
doi: 10.3390/biomedicines9070843
pmc: PMC8301818
pii:
doi:
Types de publication
Journal Article
Langues
eng
Références
J Alzheimers Dis. 2018;62(2):713-725
pubmed: 29480173
J Am Heart Assoc. 2020 Feb 18;9(4):e013924
pubmed: 32067584
Pulm Circ. 2017 Apr-Jun;7(2):486-493
pubmed: 28597780
Int J Cardiol. 2018 Jul 1;262:57-63
pubmed: 29622508
J Am Coll Cardiol. 2014 Oct 28;64(17):1765-74
pubmed: 25443696
Am J Hypertens. 2020 Dec 31;33(12):1067-1070
pubmed: 32965491
Am J Cardiol. 2019 Sep 15;124(6):871-878
pubmed: 31350002
J Am Coll Cardiol. 2020 Apr 7;75(13):1523-1534
pubmed: 32241367
Int J Cardiol. 2021 Jan 15;323:83-89
pubmed: 32800908
JAMA Cardiol. 2018 Jan 1;3(1):54-63
pubmed: 29128866
Cardiovasc Res. 2021 Apr 26;:
pubmed: 33913486
Intern Emerg Med. 2020 Oct;15(7):1183-1192
pubmed: 32557091
Chest. 2018 Nov;154(5):1121-1201
pubmed: 30144419
Circulation. 2015 Jan 20;131(3):269-79
pubmed: 25398313
PLoS One. 2016 Feb 03;11(2):e0145881
pubmed: 26840410
Stroke. 2021 Jan;52(2):521-530
pubmed: 33423512
Brain Sci. 2020 Jul 02;10(7):
pubmed: 32630627
Thromb Haemost. 2021 Feb;121(2):140-149
pubmed: 32920808
J Am Coll Cardiol. 2017 Oct 3;70(14):1704-1716
pubmed: 28958326
Eur J Clin Invest. 2021 Jun;51(6):e13498
pubmed: 33482011
Intern Emerg Med. 2021 Aug;16(5):1131-1140
pubmed: 33161524
Lancet. 2008 Jan 26;371(9609):315-21
pubmed: 18294998
Eur Heart J. 2021 Feb 1;42(5):373-498
pubmed: 32860505
Europace. 2021 Feb 5;23(2):174-183
pubmed: 33006613
J Am Heart Assoc. 2020 May 18;9(10):e014932
pubmed: 32370588
Cardiovasc Res. 2021 May 25;117(6):1420-1422
pubmed: 33175134
Eur J Intern Med. 2021 Apr;86:1-11
pubmed: 33518403
Eur J Intern Med. 2018 Sep;55:28-34
pubmed: 29778588
Circ Cardiovasc Imaging. 2020 May;13(5):e009707
pubmed: 32418453
J Thromb Haemost. 2014 Jun;12(6):824-30
pubmed: 24597472