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.