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Advanced echocardiography and cluster analysis to identify secondary tricuspid regurgitation phenogroups at different risk

Authors
 Badano, Luigi P.  ;  Penso, Marco  ;  Tomaselli, Michele  ;  Kim, Kyu  ;  Clement, Alexandra  ;  Radu, Noela  ;  Hong, Geu-Ru  ;  Hadareanu, Diana R.  ;  Buta, Alexandra  ;  Delcea, Caterina  ;  Fisicaro, Samantha  ;  Parati, Gianfranco  ;  Shim, Chi Young  ;  Murarua, Denisa 
Citation
 REVISTA ESPANOLA DE CARDIOLOGIA, Vol.78(10) : 838-847, 2025-10 
Journal Title
REVISTA ESPANOLA DE CARDIOLOGIA
ISSN
 0300-8932 
Issue Date
2025-10
MeSH
Aged ; Cluster Analysis ; Echocardiography* / methods ; Female ; Follow-Up Studies ; Humans ; Male ; Middle Aged ; Prognosis ; Retrospective Studies ; Risk Assessment / methods ; Risk Factors ; Severity of Illness Index ; Tricuspid Valve Insufficiency* / diagnosis ; Tricuspid Valve Insufficiency* / diagnostic imaging ; Tricuspid Valve Insufficiency* / etiology ; Tricuspid Valve Insufficiency* / mortality ; Tricuspid Valve Insufficiency* / physiopathology
Keywords
Secondary tricuspid regurgitation ; Unsupervised cluster analysis ; Phenogroups ; Outcomes ; Machine learning ; 3-dimensional echocardiography ; Speckle-tracking echocardiography
Abstract
Introduction and objectives: Significant secondary tricuspid regurgitation (STR) is associated with poor prognosis, but its heterogeneity makes predicting patient outcomes challenging. Our objective was to identify STR prognostic phenogroups. Methods: We analyzed 758 patients with moderate-to-severe STR: 558 (74 +/- 14 years, 55% women) in the derivation cohort and 200 (73 +/- 12 years, 60% women) in the external validation cohort. The primary endpoint was a composite of heart failure hospitalization and all-cause mortality. Results: We identified 3 phenogroups. The low-risk phenogroup (2-year event-free survival 80%, 95%CI, 74%-87%) had moderate STR, preserved right ventricular (RV) size and function, and a moderately dilated but normally functioning right atrium. The intermediate-risk phenogroup (HR, 2.20; 95%CI, 1.44-3.37; P < .001) included older patients with severe STR, and a mildly dilated but uncoupled RV. The high-risk phenogroup (HR, 4.67; 95%CI, 3.20-6.82; P < .001) included younger patients with massive-to-torrential tricuspid regurgitation, as well as severely dilated and dysfunctional RV and right atrium. Multivariable analysis confirmed the clustering as independently associated with the composite endpoint (HR, 1.40; 95%CI, 1.13-1.70; P = .002). A supervised machine learning model, developed to assist clinicians in assigning patients to the 3 phenogroups, demonstrated excellent performance both in the derivation cohort (accuracy = 0.91, precision = 0.91, recall = 0.91, and F1 score = 0.91) and in the validation cohort (accuracy = 0.80, precision = 0.78, recall = 0.78, and F1 score = 0.77). Conclusions: The unsupervised cluster analysis identified 3 risk phenogroups, which could assist clinicians in developing more personalized treatment and follow-up strategies for STR patients. (c) 2025 Sociedad Espa & ntilde;ola de Cardiologia. Published by Elsevier Espa & ntilde;a, S.L.U. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Files in This Item:
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DOI
10.1016/j.rec.2025.02.004
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Shim, Chi Young(심지영) ORCID logo https://orcid.org/0000-0002-6136-0136
Hong, Geu Ru(홍그루) ORCID logo https://orcid.org/0000-0003-4981-3304
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/209240
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