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.
Machine learning and computational modeling have become increasingly pervasive in medicine. The goal of these projects is to combine the two into a clinical risk prediction algorithm, in what we call the Computational Heart and Artificial Intelligence (CHAI) approach. Preliminary work has focused on both atrial and ventricular modeling and demonstrates that the CHAI approach out-performs common clinical risk stratification methodologies.
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.
Current risk stratification criteria are inadequate in identifying patients with hypertrophic cardiomyopathy (HCM) at risk for ventricular tachycardia (VT) and in need of an implantable cardioverter defibrillator (ICD). Our personalized virtual heart technology incorporates a fusion of contrast-enhanced LGE-MRI and post-contrast T1 mapping to reconstruct the fibrotic remodeling that occurs in HCM. In silico rapid pacing is used to assess VT inducibility which may improve the identification of patients with HCM in need of an ICD.
The goal of this project is to study the effect of myofiber architecture on wave propagation in human atria using image-based simulations. To investigate this, we use models with subject-specific geometry and fiber orientation constructed from ex vivo diffusion tensor (DT)- MRI scans of human hearts.
In the literature:
Farhad Pashakhanloo, et al. "Image-based models of whole human atria with subject-specific geometry and fiber orientation reveal the influence of myofiber architecture on anisotropic wave propagation", Heart Rhythm Society Scientific Sessions, Chicago, IL, 2017 (Oral presentation)
Progression of mitochondrial network failure is characterized by the increase in extent of depolarizing mitochondria. We used a computational model to investigate how depolarization vulnerability across the network is acquired. We found that gradual ROS accumulation and scavenging capacity depletion across the network precedes growth of depolarization vulnerability. Further, communication of oxidative stress via the ROS hydrogen peroxide was necessary for these processes to occur. The time course for the transport of hydrogen peroxide and the resulting accumulation/depletion processes determines the time course for the evolution of mitochondrial failure.
This project explores comparison between reentrant driver (RD)-harboring regions identified by electrocardiographic imaging (ECGI), conducted prior to catheter ablation in persistent atrial fibrillation (PsAF) patients, and via simulations conducted in patient-specific computational models reconstructed from late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) scans. Our discovery of atrial regions in which both ECGI and simulations detected RDs suggests that PsAF is at least partially driven by fibrosis-mediated mechanisms. Simulations also identify "latent" RDs – regions within the fibrotic substrate where an RD could persist, but never manifested during clinical mapping. Conversely, RD-harboring regions identified by ECGI but not in simulations indicate that some clinically mapped AF episodes were perpetuated by mechanisms other than the fibrotic substrate. Our retrospective analysis suggests that substrate-based ablation combining simulations with ECGI could improve outcomes.
Biophysical simulation of a whole human heart has been driving the forefront of mathematical modeling and computing technology. We are developing electromechanical heart models individualized to image scans of individual patients. Such heart models incorporate structural, mechanical and electrophysiological remodeling effects associated with myocardial infarction from the molecular level up to the organ level. Our goal is to use this novel computational model to elucidate how myocardial infarction influences dyssynchronous heart failure, and thereby to help doctors improve the efficacy of the current clinical treatment approach, cardiac resynchronization therapy (CRT), which is not effective for 30% of heart failure patients who receive the CRT.
Personalized heart models were generated from patients' contrast enhanced MRI scans by extrapolating ventricular geometry over the ICD artifact region; the latter was assumed to not contain infarcted tissue while outside the artifact, tissues were classified as normal, scar, and gray zone based on pixel intensity (Fig line 1). Application of Virtual-heart Arrhythmia Prediction (VARP) protocol from 26 sites induced in silico VT, and VT analysis predicted optimal ablation targets (Fig line 2). These targets coincided with the clinical ablation lesions but were smaller. Personalized ventricular models could accurately predict non-invasively the optimal VT ablation sites where scar was not obscured by ICD artifact, and thus might be used to guide clinical ablation.