Right heart failure and patient selection for isolated tricuspid valve surgery.

OBJECTIVE: To characterize patients with right heart failure undergoing isolated tricuspid valve surgery, focusing on right heart morphology and function. PATIENTS AND METHODS: From January 2007 to January 2014, 62 patients underwent isolated tricuspid valve surgery. Forty-five patients (73%) had undergone previous heart operations. Right heart morphology and function variables were measured de novo from stored echocardiographic images, and clinical and hemodynamic data were extracted from patient registries and records. Cluster analysis was performed and outcomes assessed. RESULTS: On average, the right ventricle was dilated (diastolic area 32 cm2), but its function was preserved (free-wall strain -17% ± 5.8%) and right heart failure manifestations were moderate, with 40 (65%) having congested neck veins, 35 (56%) dependent edema, and 15 (24%) ascites. Average model for end-stage liver disease with sodium score was 11 ± 4.4, but individual values varied widely. Tricuspid valve variables split patients into 2 equal clusters: those with functional tricuspid regurgitation (TR) and those with structural TR. These groups had similar right ventricular function, but the functional TR group had worse right ventricular morphology and more severe manifestations of right heart failure, including greater model for end-stage liver disease with sodium scores (12 ± 44 vs 9.1 ± 3.9; P = .008). Both groups survived operation with low morbidity, but patients with functional TR had worse long-term survival, 48% versus 73% at 10 years after surgery. CONCLUSIONS: The cluster analysis of patients with right heart failure undergoing isolated tricuspid valve surgery separated functional and structural tricuspid valve disease. Good early outcomes suggest expanding criteria for tricuspid valve surgery and earlier intervention for functional TR with right heart failure.

Machine-learning phenotypic classification of bicuspid aortopathy.

BACKGROUND: Bicuspid aortic valves (BAV) are associated with incompletely characterized aortopathy. Our objectives were to identify distinct patterns of aortopathy using machine-learning methods and characterize their association with valve morphology and patient characteristics. METHODS: We analyzed preoperative 3-dimensional computed tomography reconstructions for 656 patients with BAV undergoing ascending aorta surgery between January 2002 and January 2014. Unsupervised partitioning around medoids was used to cluster aortic dimensions. Group differences were identified using polytomous random forest analysis. RESULTS: Three distinct aneurysm phenotypes were identified: root (n = 83; 13%), with predominant dilatation at sinuses of Valsalva; ascending (n = 364; 55%), with supracoronary enlargement rarely extending past the brachiocephalic artery; and arch (n = 209; 32%), with aortic arch dilatation. The arch phenotype had the greatest association with right-noncoronary cusp fusion: 29%, versus 13% for ascending and 15% for root phenotypes (P < .0001). Severe valve regurgitation was most prevalent in root phenotype (57%), followed by ascending (34%) and arch phenotypes (25%; P < .0001). Aortic stenosis was most prevalent in arch phenotype (62%), followed by ascending (50%) and root phenotypes (28%; P < .0001). Patient age increased as the extent of aneurysm became more distal (root, 49 years; ascending, 53 years; arch, 57 years; P < .0001), and root phenotype was associated with greater male predominance compared with ascending and arch phenotypes (94%, 76%, and 70%, respectively; P < .0001). Phenotypes were visually recognizable with 94% accuracy. CONCLUSIONS: Three distinct phenotypes of bicuspid valve-associated aortopathy were identified using machine-learning methodology. Patient characteristics and valvular dysfunction vary by phenotype, suggesting that the location of aortic pathology may be related to the underlying pathophysiology of this disease.