| Autor: |
Rao, Vishal N, Giczewska, Anna, Chiswell, Karen, Felker, G Michael, Wang, Andrew, Glower, Donald D, Gaca, Jeffrey G, Parikh, Kishan S, Vemulapalli, Sreekanth |
| Předmět: |
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| Zdroj: |
European Heart Journal; 6/1/2023, Vol. 44 Issue 21, p1910-1923, 14p |
| Abstrakt: |
Aims Severe tricuspid regurgitation (TR) exhibits high 1-year morbidity and mortality, yet long-term cardiovascular risk overall and by subgroups remains unknown. This study characterizes 5-year outcomes and identifies distinct clinical risk profiles of severe TR. Methods and results Patients were included from a large US tertiary referral center with new severe TR by echocardiography based on four-category American Society of Echocardiography grading scale between 2007 and 2018. Patients were categorized by TR etiology (with lead present, primary, and secondary) and by supervised recursive partitioning (survival trees) for outcomes of death and the composite of death or heart failure hospitalization. The Kaplan–Meier estimates and Cox regression models were used to evaluate any association by (i) TR etiology and (ii) groups identified by survival trees and outcomes over 5 years. Among 2379 consecutive patients with new severe TR, median age was 70 years, 61% were female, and 40% were black. Event rates (95% confidence interval) were 30.9 (29.0–32.8) events/100 patient-years for death and 49.0 (45.9–52.2) events/100 patient-years for the composite endpoint, with no significant difference by TR etiology. After applying supervised survival tree modeling, two separate groups of four phenoclusters with distinct clinical prognoses were separately identified for death and the composite endpoint. Variables discriminating both outcomes were age, albumin, blood urea nitrogen, right ventricular function, and systolic blood pressure (all P < 0.05). Conclusion Patients with newly identified severe TR have high 5-year risk for death and death or heart failure hospitalization. Partitioning patients using supervised survival tree models, but not TR etiology, discriminated clinical risk. These data aid in identifying relevant subgroups in clinical trials of TR and clinical risk/benefit analysis for TR therapies. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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