Effectiveness of a patient-specific 3-dimensional printed model in Septal Myectomy of hypertrophic cardiomyopathy

Three-Dimensional Heart Modeling of Hypertrophic Obstructive Cardiomyopathy for In Situ Patient-Specific Simulation to Optimize Septal Myectomy

OBJECTIVE

Hypertrophic obstructive cardiomyopathy (HOCM) develops in at least 1 out of 715 young adults. Patients who are refractory to medical therapy qualify for septal myectomy. Due to anatomy, serious complications such as ventricular septal defect and heart block may occur. Establishing cardiovascular magnetic resonance (CMR)-based 3-dimensional (3D) models as part of preoperative planning and training has the potential to decrease procedure-related complications and improve results.

METHODS

CMR images were used to segment cardiac structures. Left ventricular wall thickness was calculated and projected on top of the in silico model. A 3D model was printed with a red layer indicating a wall thickness exceeding 15 mm and used for preoperative resection planning and patient counseling. To provide preoperative patient-specific in situ simulation, the planned resection volume was replaced with silicone in a second model. For perioperative quality control, resected silicone was compared with resected myocardial tissue. The impact of the models was evaluated descriptively through consultation of both the cardiothoracic surgeon and patients and through patient outcomes.

RESULTS

Three-dimensional in silico and 3D-printed heart models of 5 patients were established preoperatively. Since the introduction of the models in October 2020, the surgeon feels better prepared, more confident, and less difficulty with making decisions. In addition, patients feel better informed preoperatively.

CONCLUSIONS

Using 3D heart models optimized preoperative planning and training, intraoperative quality control, and patient consultation. Reduction of procedure-related complications and clinical outcome should be studied in larger cohorts.

Imaging-Based, Patient-Specific Three-Dimensional Printing to Plan, Train, and Guide Cardiovascular Interventions: A Systematic Review and Meta-Analysis

BACKGROUND

To tailor cardiovascular interventions, the use of three-dimensional (3D), patient-specific phantoms (3DPSP) encompasses patient education, training, simulation, procedure planning, and outcome-prediction.

AIM

This systematic review and meta-analysis aims to investigate the current and future perspective of 3D printing for cardiovascular interventions.

METHODS

We systematically screened articles on Medline and EMBASE reporting the prospective use of 3DPSP in cardiovascular interventions by using combined search terms. Studies that compared intervention time depending on 3DPSP utilisation were included into a meta-analysis.

RESULTS

We identified 107 studies that prospectively investigated a total of 814 3DPSP in cardiovascular interventions. Most common settings were congenital heart disease (CHD) (38 articles, 6 comparative studies), left atrial appendage (LAA) occlusion (11 articles, 5 comparative, 1 randomised controlled trial [RCT]), and aortic disease (10 articles). All authors described 3DPSP as helpful in assessing complex anatomic conditions, whereas poor tissue mimicry and the non-consideration of physiological properties were cited as limitations. Compared to controls, meta-analysis of six studies showed a significant reduction of intervention time in LAA occlusion (n=3 studies), and surgery due to CHD (n=3) if 3DPSPs were used (Cohen's d=0.54; 95% confidence interval, 0.13 to 0.95; p=0.001), however heterogeneity across studies should be taken into account.

CONCLUSIONS

3DPSP are helpful to plan, train, and guide interventions in patients with complex cardiovascular anatomy. Benefits for patients include reduced intervention time with the potential for lower radiation exposure and shorter mechanical ventilation times. More evidence and RCTs including clinical endpoints are needed to warrant adoption of 3DPSP into routine clinical practice.

An Implementation of Patient-Specific Biventricular Mechanics Simulations With a Deep Learning and Computational Pipeline

Parameterised patient-specific models of the heart enable quantitative analysis of cardiac function as well as estimation of regional stress and intrinsic tissue stiffness. However, the development of personalised models and subsequent simulations have often required lengthy manual setup, from image labelling through to generating the finite element model and assigning boundary conditions. Recently, rapid patient-specific finite element modelling has been made possible through the use of machine learning techniques. In this paper, utilising multiple neural networks for image labelling and detection of valve landmarks, together with streamlined data integration, a pipeline for generating patient-specific biventricular models is applied to clinically-acquired data from a diverse cohort of individuals, including hypertrophic and dilated cardiomyopathy patients and healthy volunteers. Valve motion from tracked landmarks as well as cavity volumes measured from labelled images are used to drive realistic motion and estimate passive tissue stiffness values. The neural networks are shown to accurately label cardiac regions and features for these diverse morphologies. Furthermore, differences in global intrinsic parameters, such as tissue anisotropy and normalised active tension, between groups illustrate respective underlying changes in tissue composition and/or structure as a result of pathology. This study shows the successful application of a generic pipeline for biventricular modelling, incorporating artificial intelligence solutions, within a diverse cohort.