Unsupervised cluster analysis reveals different phenotypes in patients after transcatheter aortic valve replacement

Kenya Kusunose; Takumasa Tsuji; Yukina Hirata; T Takahashi; Masataka Sata; Kimi Sato; Noor Albakaa; Tomoko Ishizu; J. Kotoku; Yoshihiro Seo; Masaki Izumo; Atsushi Okada; Chisato Izumi; Shu Inami; Yasuharu Takeda; Toshinari Onishi; Yuki Izumi; Akiko Kumagai; Tomoko Fukuda; Naohiko Takahashi; Takeshi Kitai; Hiroyuki Iwano; Shigeo Sugawara; Kazumi Akasaka; Kenji Harada; Yoshiko Masaoka; Kazuaki Tanabe; Takahiro Sakamoto; Takeshi Takamura

doi:10.1093/ehjopen/oead136

Why the study?

The study aimed to identify phenotypes with potential prognostic significance in aortic stenosis patients after TAVR using an unsupervised clustering approach.

Does unsupervised cluster analysis identify phenotypes with prognostic significance in patients with severe AS after TAVR?

Structured PICO

Does unsupervised cluster analysis identify phenotypes with prognostic significance in patients with severe AS after TAVR?

P

Population

1365 patients with severe aortic stenosis (AS) who underwent transcatheter aortic valve replacement (TAVR) between January 2015 and March 2019.

I

Intervention

Unsupervised cluster analysis based on 20 demographic, laboratory, and echocardiography variables

C

Comparator

Comparison among three identified clusters (Cluster 1: elderly, high gradient, LVH; Cluster 2: preserved LVEF, larger valve area, high BP; Cluster 3: tachycardia, low flow/low gradient AS)

O

Outcome

Adverse outcomes

Unsupervised cluster analysis identifies distinct clinical and echocardiographic phenotypes in post-TAVR patients, with a low flow/low gradient and tachycardia phenotype (Cluster 3) showing significantly worse prognosis.

Implication

May support phenotype-based prognostication after TAVR; leaves open prospective validation before clinical use.

Abstract

Aims The aim of this study was to identify phenotypes with potential prognostic significance in aortic stenosis (AS) patients after transcatheter aortic valve replacement (TAVR) through a clustering approach. Methods and results This multi-centre retrospective study included 1365 patients with severe AS who underwent TAVR between January 2015 and March 2019. Among demographics, laboratory, and echocardiography parameters, 20 variables were selected through dimension reduction and used for unsupervised clustering. Phenotypes and outcomes were compared between clusters. Patients were randomly divided into a derivation cohort (n = 1092: 80%) and a validation cohort (n = 273: 20%). Three clusters with markedly different features were identified. Cluster 1 was associated predominantly with elderly age, a high aortic valve gradient, and left ventricular (LV) hypertrophy; Cluster 2 consisted of preserved LV ejection fraction, larger aortic valve area, and high blood pressure; and Cluster 3 demonstrated tachycardia and low flow/low gradient AS. Adverse outcomes differed significantly among clusters during a median of 2.2 years of follow-up (P < 0.001). After adjustment for clinical and echocardiographic data in a Cox proportional hazards model, Cluster 3 (hazard ratio, 4.18; 95% confidence interval, 1.76–9.94; P = 0.001) was associated with increased risk of adverse outcomes. In sequential Cox models, a model based on clinical data and echocardiographic variables (χ2 = 18.4) was improved by Cluster 3 (χ2 = 31.5; P = 0.001) in the validation cohort. Conclusion Unsupervised cluster analysis of patients after TAVR revealed three different groups for assessment of prognosis. This provides a new perspective in the categorization of patients after TAVR that considers comorbidities and extravalvular cardiac dysfunction.