ARVC is an inherited cardiac disease that increases risk of life-threatening VTs in young adults. Catheter ablation, a mainstay of VT treatment in ARVC, remains challenging due to difficulties in identifying optimal VT ablation targets. We developed a novel image-based personalized digital-heart paradigm to accurately predict the optimal VT ablation targets of ARVC patients.
MRI-based personalized virtual heart models(VHM) are a useful tool for reconstructing scar distribution and identifying VT circuits for planning catheter ablation procedure, an effective therapy for preventing sudden cardiac death from VT in patients with myocardial infarction.However, the MRI resolution used to reconstruct VHMs may not be sufficient to accurately detect all VT circuits, possibly leading to erroneous predictions. We aim to predict catheter ablation targets in the arrhythmogenic substrate using VHMs reconstructed from both clinical and high-resolution 2D MRI datasets and compare the predictions.
Transposition of Greater Arteries (TGA) is a rare heart condition that affects about 1 in 3500 babies in the USA. In this condition, the two main blood vessels leaving the heart (the aorta and the pulmonary artery) are in abnormal positions. TGA is often remediated by arterial switch surgery. However, patients suffering from TGA have long-term complications and a reduced lifespan. TGA patients undergo MRI scans multiple times to reassess their heart condition. Our work focuses on understanding underlying patterns in TGA clinical data to provide better clinical insights for clinicians.
The need for fast simulations is a priority in computational cardiology. Cardiac simulations rely upon heavy computational calculations using finite element methods which take a long time on top performing machines. We are implementing neural networks to overcome this challenge which have the potential to surrogate finite element solutions. Through applying graph theories, we can teach a neural network to solve electrophysiological differential equations with relatively low errors for simulating atrial fibrillation.
Current clinical criteria outlined in AHA/ACC and ESC guidelines remain inadequate for accurately identifying hypertrophic cardiomyopathy (HCM) patients at risk for sudden cardiac death (SCD) by ventricular arrhythmia (VA). Although many risk factors for SCD in HCM patients have been established individually, including demographic information, lab tests, late gadolinium enhanced magnetic resonance imaging (LGE-MRI) features, it is challenging to integrate these distinct types of data in a predictive model. We developed a multi-model deep learning model to integrate raw LGE-MRI images and clinical covariates for automated risk stratification of VA in HCM patients. Results suggest that our model significantly improved the prediction of VA in HCM patients compared with single-modal models and current clinical guidelines. It could enable high-precision care for HCM patients at high risk for SCD.
We demonstrated the utility of virtual-heart simulations in determining noninvasively the optimal ventricular tachycardia (VT) ablation targets and guiding the clinical procedure of VT ablation (https://doi.org/10.1038/s41551-018-0282-2). The non-invasive approach was termed VAAT (virtual-heart arrhythmia ablation targeting) and was assessed in retrospective studies, and in a proof-of-concept prospective study.
We now proceed with AVERT-VT; FDA-approved clinical trial for 10 prospective patients to demonstrate decreased procedural times, decreased patient risk, and improved efficacy for the catheter ablation management of infarct-related VT. Personalized 3D ventricular models are reconstructed from patient’s cardiac LGE-MRI imaging data. VAAT protocol is repeated until all in silico VTs are terminated by the virtual lesions and complete VT non‐inducibilityis achieved. The set of ablation targets that achieve this are the final (optimal) set of VAAT ablation targets; representing the targets that would be directly approached during the clinical procedure, without any electrical mapping (https://doi.org/10.1002/wsbm.1477).
Presence of left atrial (LA) fibrosis burden increases the stroke risk in atrial fibrillation (AF) patients. However, exact reasons as of how the increased LA fibrotic burden increases stroke risk remains unknown. We hypothesize that presence of fibrosis could lead to aberrant hemodynamics in the LA of AF patients, which could explain their increased stroke risk. We used personalized LA hemodynamic simulations on LGE-MRI images of AF patients to explore flow-dynamics in the fibrotic and compared to the non-fibrotic region at the LA wall. Preliminary results suggest that flow in the LA near the fibrotic region is aberrant, potentially leading to thrombogenesis and increasing the risk of stroke
To accurately characterize disease progression and quantify pathophysiological remodeling in the heart, clinical cardiology employs cardiac magnetic resonance (CMR) imaging with contrast enhancement (LGE) to visualize scarring and fibrosis in the ventricles. However, LGE-CMR image analysis is a labor-intensive process prone to large inter-observer variability that requires significant training and expertise. We developed a novel fully-automated anatomically-informed deep learning solution for LV and scar/fibrosis segmentation and clinical feature extraction from LGE-CMR.
AF patients are at fivefold higher risk for stroke than normal population. LAA closure procedure is most common to reduce risk of stroke for AF patients. We have developed a personalized blood-flow analysis based stroke risk predictor for AF patients undergoing LAA closure. In a proof-of-concept study on four AF patients undergoing LAA closure procedure, we show that for patients having stroke or transient ischemic attack (TIA) after the procedure, the LAA closure may not substantially reduce low-flow zone in the LA, predisposing them to risk of future stroke/TIA. The stroke prediction tool could potentially identify such cases, enabling a priori assessment of the efficacy of LAA closure devices for AF patients, as well as close monitoring of patients after LAA closure.
In a proof-of-concept study, published in Nature Biomedical Engineering, we have demonstrated the utility of personalized computational atrial modeling to guide ablation of persistent AF (psAF). This novel technology (termed OPTIMA, OPtimal Target Identification via Modeling of Arrhythmogenesis) is based on non-invasive patient-specific anatomic and tissue data from late gadolinium enhancement cardiac MRI (LGE-CMR) and simulation of cardiac electrical function to personalized ablation targets for psAF patients.
We now have FDA-approval for a 160-patient randomized clinical trial to demonstrate the utility of OPTIMA in patients with persistent atrial fibrillation and fibrosis. The approach is termed OPTIMA. The PIs are Drs. Trayanova, Calkins, and Spraag.