Clinical value of CT-derived simulations of transcatheter-aortic-valve-implantation in challenging anatomies the PRECISE-TAVI trial

Accuracy of Three-Dimensional Neo Left Ventricular Outflow Tract Simulations With Transcatheter Mitral Valve Replacement in Different Mitral Phenotypes

BACKGROUND

Transcatheter mitral valve replacement (TMVR) is emerging in the context of annular calcification (valve-in-MAC; ViMAC), failing surgical mitral annuloplasty (mitral-valve-in-ring; MViR) and failing mitral bioprosthesis (mitral-valve-in-valve; MViV). A notorious risk of TMVR is neo left ventricular outflow tract (neo-LVOT) obstruction. Three-dimensional computational models (3DCM) are derived from multi-slice computed tomography (MSCT) and aim to predict neo-LVOT area after TMVR. Little is known about the accuracy of these neo-LVOT predictions for various mitral phenotypes.

METHODS

Preprocedural 3DCMs were created for ViMAC, MViR and MViV cases. Throughout the cardiac cycle, neo-LVOT dimensions were semi-automatically calculated on the 3DCMs. We compared the predicted neo-LVOT area on the preprocedural 3DCM with the actual neo-LVOT as measured on the post-procedural MSCT.

RESULTS

Across 12 TMVR cases and examining 20%-70% of the cardiac phase, the mean difference between predicted and post-TMVR neo-LVOT area was -23 ± 28 mm2 for MViR, -21 ± 34 mm2 for MViV and -73 ± 61 mm2 for ViMAC. The mean intra-class correlation coefficient for absolute agreement between predicted and post-procedural neo-LVOT area (throughout the whole cardiac cycle) was 0.89 (95% CI 0.82-0.94, p < 0.001) for MViR, 0.81 (95% CI 0.62-0.89, p < 0.001) for MViV, and 0.41 (95% CI 0.12-0.58, p = 0.002) for ViMAC.

CONCLUSIONS

Three-dimensional computational models accurately predict neo-LVOT dimensions post TMVR in MViR and MViV but not in ViMAC. Further research should incorporate device host interactions and the effect of changing hemodynamics in these simulations to enhance accuracy in all mitral phenotypes.

CT-derived simulations to predict outcomes in patients undergoing transcatheter aortic valve implantation with an ACURATE Neo2 valve the PRECISE-TAVI cohort B trial

BACKGROUND

Paravalvular leakage (PVL) and conduction disorders that require permanent pacemaker implantation (PPI) remain clinically relevant challenges after transcatheter aortic valve implantation (TAVI). Computed tomography-based simulations may predict the risk of significant PVL and PPI.

AIMS

To evaluate the feasibility and accuracy of preprocedural computer simulation with FEops HEARTguide™ to predict >trace PVL and PPI after TAVI with the self-expanding supra-annular ACURATE Neo2 transcatheter heart valve.

METHODS

Prospective multicenter observational study that included consecutive patients undergoing TAVI with an ACURATE Neo2 valve. Computer simulations were performed before the TAVI procedure as part of the preprocedural planning. Follow-up period for PPI and PVL was 30 days.

RESULTS

Sixty-five patients were included (median age 81 years (25th-75th percentile 77-84.5)). New left bundle branch block occurred in five patients (7.7%) and PPI in two patients (3%). Contact pressure index (CPI) was similar for patients with vs without new conduction disorders. Patients with PPI had numerically higher CPI than those without PPI (median CPI 20.0% (25th-75th percentile 15.0-25.0) vs. 13.0% (25th-75th percentile 5.5-18), p = 0.27). More than trace PVL occurred in 30%. Median PVL was significantly lower in patients with none-trace PVL (3.2 mL/s [25th-75th percentile 2.2-5.0]), compared to mild PVL (5.2 mL/s [25th-75th percentile 3.2-10.3]) and moderate PVL (12.6 mL/s [25th-75th percentile 3.9-21.3])(p = 0.036). A simulated PVL-cutoff of 9.65 mL/s identified patients with >trace PVL (AUC 0.70 (95% CI 0.55-0.85), sensitivity 42%, specificity 95%).

CONCLUSION

In our study FEops HEARTguide™ simulations identified patients at risk for >trace PVL with ACURATE Neo2 TAVI but not for PPI.

Predicting new-onset persistent conduction disturbance following transcatheter aortic valve replacement: the usefulness of FEOPS finite element analysis

BACKGROUND

Despite the frequency of persistent new-onset conduction disturbances after transcatheter aortic valve replacement (TAVR), few preoperative methods of prediction exist.

METHODS

Patients who underwent TAVR in the Department of Cardiology of the Second Affiliated Hospital of the Army Medical University from December 2020 to September 2021 and postoperative aortic root modeling via the FEOPS finite element analysis were included in this single-center case-control study, divided into persistent conduction disturbances (PCD) and non-PCD groups according to their pre- and postoperative electrocardiograms in the first month. Risk factors affecting PCD were identified by comparing the baseline data of these two groups, including echocardiograms, computed tomography angiography of the aortic root, surgical decision-making, and FEOPS data. Independent risk factors were screened using logistic regression modeling, and the receiver operating characteristic (ROC) curve was used to test the predictive ability.

RESULTS

A total of 56 patients were included in this study, 37 with bicuspid aortic valve (BAV) and 19 with trileaflet aortic valve (TAV), with 17 cases of PCD. The contact pressure index (CPI) of FEOPS, valve oversize ratio, differences between membranous interventricular septum length and implantation depth (ΔMSID) and valve implantation depth were statistically different (P < 0.05). CPI could be used as an independent risk factor for PCD (P < 0.05), and the ROC curve comparison showed that the CPI was more predictive (AUC = 0.806, 95% CI: 0.684-0.928, P = 0.001).

CONCLUSIONS

The CPI of FEOPS has better predictive value for new-onset conduction disturbance after TAVR compared to other known predictors.

The Current Landscape of Artificial Intelligence in Imaging for Transcatheter Aortic Valve Replacement

Purpose

This review explores the current landscape of AI applications in imaging for TAVR, emphasizing the potential and limitations of these tools for (1) automating the image analysis and reporting process, (2) improving procedural planning, and (3) offering additional insight into post-TAVR outcomes. Finally, the direction of future research necessary to bridge these tools towards clinical integration is discussed.

Recent Findings

Transcatheter aortic valve replacement (TAVR) has become a pivotal treatment option for select patients with severe aortic stenosis, and its indication for use continues to broaden. Noninvasive imaging techniques such as CTA and MRA have become routine for patient selection, preprocedural planning, and predicting the risk of complications. As the current methods for pre-TAVR image analysis are labor-intensive and have significant inter-operator variability, experts are looking towards artificial intelligence (AI) as a potential solution.

Summary

AI has the potential to significantly enhance the planning, execution, and post-procedural follow up of TAVR. While AI tools are promising, the irreplaceable value of nuanced clinical judgment by skilled physician teams must not be overlooked. With continued research, collaboration, and careful implementation, AI can become an integral part in imaging for TAVR, ultimately improving patient care and outcomes.

Challenges for augmenting intelligence in cardiac imaging

Artificial Intelligence (AI), through deep learning, has brought automation and predictive capabilities to cardiac imaging. However, despite considerable investment, tangible health-care cost reductions remain unproven. Although AI holds promise, there has been insufficient time for both methodological development and prospective clinical trials to establish its advantage over human interpretations in terms of its effect on patient outcomes. Challenges such as data scarcity, privacy issues, and ethical concerns impede optimal AI training. Furthermore, the absence of a unified model for the complex structure and function of the heart and evolving domain knowledge can introduce heuristic biases and influence underlying assumptions in model development. Integrating AI into diverse institutional picture archiving and communication systems and devices also presents a clinical hurdle. This hurdle is further compounded by an absence of high-quality labelled data, difficulty sharing data between institutions, and non-uniform and inadequate gold standards for external validations and comparisons of model performance in real-world settings. Nevertheless, there is a strong push in industry and academia for AI solutions in medical imaging. This Series paper reviews key studies and identifies challenges that require a pragmatic change in the approach for using AI for cardiac imaging, whereby AI is viewed as augmented intelligence to complement, not replace, human judgement. The focus should shift from isolated measurements to integrating non-linear and complex data towards identifying disease phenotypes-emphasising pattern recognition where AI excels. Algorithms should enhance imaging reports, enriching patients' understanding, communication between patients and clinicians, and shared decision making. The emergence of professional standards and guidelines is essential to address these developments and ensure the safe and effective integration of AI in cardiac imaging.

Evolving capabilities of computed tomography imaging for transcatheter valvular heart interventions - new opportunities for precision medicine

The last decade has witnessed a substantial growth in percutaneous treatment options for heart valve disease. The development in these innovative therapies has been mirrored by advances in multi-detector computed tomography (MDCT). MDCT plays a central role in obtaining detailed pre-procedural anatomical information, helping to inform clinical decisions surrounding procedural planning, improve clinical outcomes and prevent potential complications. Improvements in MDCT image acquisition and processing techniques have led to increased application of advanced analytics in routine clinical care. Workflow implementation of patient-specific computational modeling, fluid dynamics, 3D printing, extended reality, extracellular volume mapping and artificial intelligence are shaping the landscape for delivering patient-specific care. This review will provide an insight of key innovations in the field of MDCT for planning transcatheter heart valve interventions.

The future of valvular heart disease assessment and therapy

Valvular heart disease (VHD) is becoming more prevalent in an ageing population, leading to challenges in diagnosis and management. This two-part Series offers a comprehensive review of changing concepts in VHD, covering diagnosis, intervention timing, novel management strategies, and the current state of research. The first paper highlights the remarkable progress made in imaging and transcatheter techniques, effectively addressing the treatment paradox wherein populations at the highest risk of VHD often receive the least treatment. These advances have attracted the attention of clinicians, researchers, engineers, device manufacturers, and investors, leading to the exploration and proposal of treatment approaches grounded in pathophysiology and multidisciplinary strategies for VHD management. This Series paper focuses on innovations involving computational, pharmacological, and bioengineering approaches that are transforming the diagnosis and management of patients with VHD. Artificial intelligence and digital methods are enhancing screening, diagnosis, and planning procedures, and the integration of imaging and clinical data is improving the classification of VHD severity. The emergence of artificial intelligence techniques, including so-called digital twins-eg, computer-generated replicas of the heart-is aiding the development of new strategies for enhanced risk stratification, prognostication, and individualised therapeutic targeting. Various new molecular targets and novel pharmacological strategies are being developed, including multiomics-ie, analytical methods used to integrate complex biological big data to find novel pathways to halt the progression of VHD. In addition, efforts have been undertaken to engineer heart valve tissue and provide a living valve conduit capable of growth and biological integration. Overall, these advances emphasise the importance of early detection, personalised management, and cutting-edge interventions to optimise outcomes amid the evolving landscape of VHD. Although several challenges must be overcome, these breakthroughs represent opportunities to advance patient-centred investigations.

Patient-specific computer simulation of transcatheter aortic valve replacement in patients with previous mechanical mitral prosthesis: A case series

Transcatheter aortic valve replacement performed in patients with previously implanted mechanical mitral prosthesis represents a high risk procedure with several potential complications. We report a systematic use of a prediction model based on artificial intelligence to plan the interventional strategy in this challenging scenario.