Valve deterioration: A victim of construct over time?

Surgical aortic valve replacement (sAVR) remains one of the most common cardiac operations performed globally on an annual basis. Biological and mechanical valves comprise the two classes of prosthetic valves available to surgeons. Biological prosthetic valves can be prone to failure and structural valve deterioration (SVD), which may necessitate reintervention. Recent literature suggests that the Trifecta heart valve is susceptible to early failure. In this retrospective study, Yount et al. use institutional data to assess the longevity of the Trifecta heart valve. The investigators included patients who had undergone sAVR and had received either a Trifecta prosthetic heart valve or a Magna/Magna Ease heart valve. While there were some baseline differences between the patient groups, the study found that those who had received a Trifecta valve had higher rates of valve failure. This is an important study that adds valuable evidence pertaining to the incidence of failure and SVD with the Trifecta heart valve. Although further studies may shed light on the precise mechanisms that drive valve failure and deterioration, surgeons should be aware of the mounting clinical data in this area.

Aortic and mitral flow quantification using dynamic valve tracking and machine learning: Prospective study assessing static and dynamic plane repeatability, variability and agreement.

BACKGROUND: Blood flow is a crucial measurement in the assessment of heart valve disease. Time-resolved flow using magnetic resonance imaging (4 D flow MRI) can provide a comprehensive assessment of heart valve hemodynamics but it relies in manual plane analysis. In this study, we aimed to demonstrate the feasibility of automate the detection and tracking of aortic and mitral valve planes to assess blood flow from 4 D flow MRI. METHODS: In this prospective study, a total of n = 106 subjects were enrolled: 19 patients with mitral disease, 65 aortic disease patients and 22 healthy controls. Machine learning was employed to detect aortic and mitral location and motion in a cine three-chamber plane and a perpendicular projection was co-registered to the 4 D flow MRI dataset to quantify flow volume, regurgitant fraction, and a peak velocity. Static and dynamic plane association and agreement were evaluated. Intra- and inter-observer, and scan-rescan reproducibility were also assessed. RESULTS: Aortic regurgitant fraction was elevated in aortic valve disease patients as compared with controls and mitral valve disease patients (p < 0.05). Similarly, mitral regurgitant fraction was higher in mitral valve patients (p < 0.05). Both aortic and mitral total flow were high in aortic patients. Static and dynamic were good (r > 0.6, p < 0.005) for aortic total flow and peak velocity, and mitral peak velocity and regurgitant fraction. All measurements showed good inter- and intra-observer, and scan-rescan reproducibility. CONCLUSION: We demonstrated that aortic and mitral hemodynamics can efficiently be quantified from 4 D flow MRI using assisted valve detection with machine learning.