A review of numerical simulation in transcatheter aortic valve replacement decision optimization

Virtual Therapy Planning of Aortic Valve Replacement for Preventing Patient-Prosthesis Mismatch

BACKGROUND

Recent studies suggest that any degree of patient-prosthesis mismatch (PPM) increases morbidity and mortality after surgical aortic valve replacement (SAVR). We used computational fluid dynamics simulations to test the influence of prosthesis size and physical activity after SAVR.

METHODS

In 10 patients with aortic valve stenosis, virtual SAVR was performed. Left ventricular outflow tract stroke volume and flow direction information (4D Flow) were used, and an increase in stroke volume of 25% was chosen for simulating physical activity. Pressure gradients (DP max) across the aortic valve and blood flow profiles in the ascending aorta were calculated and predicted for three different valve sizes at rest and under stress in every patient.

RESULTS

Gradients across the aortic valve were significantly lower using larger valves; however, they were not normalized after SAVR (DP max [mmHg] norm/smaller/reference/larger valve = 6/14/12/9 mmHg, <0.01 compared to norm). Physical activity simulation increased DP max in all patients and across all valve sizes (DP max [mmHg] rest versus stress for the smaller/reference/larger valve = 14 vs. 23, 12 vs. 18, 9 vs. 14). Blood flow profiles did not normalize after SAVR and remained unaffected by physical activity. Gradients differed between mild and moderate stenosis between different therapy options and even showed moderate to severe stenosis under simulated physical activity.

CONCLUSIONS

Prosthesis size and physical activity simulation have a significant influence on gradients across the aortic valve. Virtual therapy planning using patient-specific data might help to improve outcomes after SAVR in the future.

Futility in TAVI: A scoping review of definitions, predictive criteria, and medical predictive models

BACKGROUND

Transcatheter Aortic Valve Implantation (TAVI) procedures are rapidly expanding, necessitating a more extensive stratification of patients with aortic stenosis. Especially in the high-risk group, some patients fail to derive optimal or any benefits from TAVI, leading to the risk of futile interventions. Despite consensus among several experts regarding the importance of recognizing and anticipating such interventions, the definition, and predictive criteria for futility in TAVI remain ambiguous.

AIM

The purpose of this study is to explore the literature addressing the definition, predictive criteria, and medical predictive models for futility in cases of TAVI.

DESIGN

A scoping review was conducted by two researchers and reported in accordance with the PRISMA-ScR guidelines.

ELIGIBILITY CRITERIA

Studies addressing futility in TAVI, including definitions, predictive variables, and models, were included without restrictions on study design but were excluded study only on surgical valve replacement, valve in valve or aortic stenosis causes by other pathology than calcification.

INFORMATION SOURCES

We identified 129 studies from five key sources: CINAHL, PUBMED, the Cochrane Library, ClinicalTrials.gov, and EMBASE. The literature search was conducted in two rounds-first in February 2024 and again in October 2024-using no restrictions on the year of publication or the language of the studies. Additional references were included through cross-referencing.

RESULTS

The definition of futility is not unanimous, although most researchers agreed on 1-year survival as a cutoff. The majority of studies focused on single variables that can predict 1-year survival, employing either prospective or retrospective designs. Frailty was the major concept studied. Numerous predictive models have been identified, but no consensus was found.

CONCLUSION

Futility concepts generate interest in the TAVI procedure. In this review, numerous articles state that 1-year mortality serves as a cutoff to define futile procedures. Some variables, cardiac or otherwise, are independent predictors of 1-year mortality. Medical predictive models showed moderate sensitivity and specificity, except for machine learning, which shows promise for the future. However, few articles delve deeply into non-quantifiable parameters such as patient goals and objectives or ethical questions. More studies should focus on these parameters.

A fast in silico model for preoperative risk assessment of paravalvular leakage

In silico simulations can be used to evaluate and optimize the safety, quality, efficacy and applicability of medical devices. Furthermore, in silico modeling is a powerful tool in therapy planning to optimally tailor treatment for each patient. For this purpose, a workflow to perform fast preoperative risk assessment of paravalvular leakage (PVL) after transcatheter aortic valve replacement (TAVR) is presented in this paper. To this end, a novel, efficient method is introduced to calculate the regurgitant volume in a simplified, but sufficiently accurate manner. A proof of concept of the method is obtained by comparison of the calculated results with results obtained from in vitro experiments. Furthermore, computational fluid dynamics (CFD) simulations are used to validate more complex stenosis scenarios. Comparing the simplified leakage model to CFD simulations reveals its potential for procedure planning and qualitative preoperative risk assessment of PVL. Finally, a 3D device deployment model and the efficient leakage model are combined to showcase the application of the presented leakage model, by studying the effect of stent size and the degree of stenosis on the regurgitant volume. The presented leakage model is also used to visualize the leakage path. To generalize the leakage model to a wide range of clinical applications, further validation on a large cohort of patients is needed to validate the accuracy of the model's prediction under various patient-specific conditions.