Identifying risk patterns in older adults with atrial fibrillation by hierarchical cluster analysis: A retrospective approach based on the risk probability for clinical events. (December 2021)

• Record Type: Journal Article
• Authors: Suzuki, Shinya; Yamashita, Takeshi; Otsuka, Takayuki; Arita, Takuto; Yagi, Naoharu; Kishi, Mikio; Semba, Hiroaki; Kano, Hiroto; Matsuno, Shunsuke; Kato, Yuko; Uejima, Tokuhisa; Oikawa, Yuji; Matsuhama, Minoru; Iida, Mitsuru; Inoue, Tatsuya; Yajima, Junji
• Is Part Of: IJC heart & vasculature. Volume 37(2021)
• Publication Date: 2021-12
• Subjects: Atrial fibrillation -- Older adults -- Cluster analysis -- Anticoagulation -- Rhythm control -- Rate control; Cardiovascular system -- Diseases -- Periodicals; Cardiovascular system -- Pathophysiology -- Periodicals; 616.1005
• DOI: 10.1016/j.ijcha.2021.100883
• Languages: English
• ISSNs: 2352-9067

Abstract

Highlights: In older AF patients, representative AF-related outcomes compete, causing difficulty in decision making. We proposed cluster analysis using risk probability for four AF-related outcomes. Older adults with AF were classified into 3 clusters. The clusters could possibly identify older adults with AF with good/poor responses to AF-related treatment. Abstract: Background: Older adults with atrial fibrillation (AF) have highly diverse risk levels for mortality, heart failure (HF), thromboembolism (TE), and major bleeding (MB), thus an integrated risk-pattern algorithm is warranted. Methods: We analyzed 573 AF patients aged ≥ 75 years from our single-center cohort (Shinken Database 2010–2018). The 3-year risk scores (risk probability) for mortality (M-score), HF (HF-score), TE (TE-score), and MB (MB-score) were estimated for each patient by logistic regression analysis. Using the four risk scores, cluster analysis was performed with Ward's linkage hierarchical algorithm. Results: Three clusters were identified: Clusters 1 (n = 429, 74%), 2 (n = 24, 5%), and 3 (n = 120, 21%). The clusters were characterized as standard risk (Cluster 1), high TE- and MB-risk (Cluster 2), and high M- and HF-risk (Cluster 3). Oral anticoagulants were prescribed for over 80% of the patients in each cluster. Catheter ablation for AF was performed only in Cluster 1 (8.9%). Compared with Cluster 1, Cluster 2 was more closely associated with males, asymptomatic AF, history of cerebral infarction or transient ischemic attack, history of intracranial hemorrhage, high HAS-BLED score (≥3), and low body mass index (<18.0 kg/m 2 ). Cluster 3 was more closely associated with old age, heart failure, and low estimated creatinine clearance (<30 mL/min). Conclusion: The cluster analysis identified those at a high risk for all-cause death and HF or a high risk for TE and MB and could support decision making in older adults with AF.