Our research projects
Simulations conducted in computational models reconstructed from MRI scans at clinical resolution have emerged as a powerful new tool in the electrophysiologist’s arsenal for treating arrhythmia and stratifying risk of future heart rhythm incidents (e.g., sudden cardiac arrest). Projects include:
- Identifying individuals with the fibrotic substrate for AF within a cohort of Embolic Stroke of Unknown Source (ESUS) patients
- Quantifying sources of uncertainty in computational simulations of the heart to enable more effective model validation and verification
- Predicting optimal ablation targets in patients with complex reentrant atrial flutter
- Developing new frameworks efficient and clinically useful assessment of arrhythmia susceptibility in patient-specific cardiac models
The lead trainee researcher in this area is Savannah Bifulco.
Ventricular tachyarrhythmias that occur following injection of heart-like cells derived from individual patients’ own stem cells (hiPSC-CM) are a huge hindrance to the feasibility of translating this exciting technology to clinical applications. Computational heart models have emerged as a powerful tool for deciphering this type of relationship.
- Characterizing robustness of spontaneous hiPSC-CM excitation in image-based computational models via cardiac safety factor (SF) calculations
- Understanding sources of triggered arrhythmia following hiPSC-CM implantation in vivo
hiPSC-CM can also be used as a means of modeling genetically inherited conditions that cause rhythm disorders. The lab is currently embarking on a new research trajectory that will involve using measurements from individual patients’ hiPSC-CM as a means of personalizing cell- and tissue-scale electrophysiological parameters in organ-scale models.
The lead trainee researcher in this area is Chelsea Gibbs.
Work in this area has led to the development of a robust computational framework for modeling optogenetics in the heart. This methodology enables simulations that can help understand how light could be used to treat arrhythmia. There is a high level of public excitement about cardiac optogenetics and great enthusiasm for further research along this trajectory.
- Using optogenetic tools to scale mechanistic insights from mouse experimental models of arrhythmia to better understand human cardiac physiology
- Developing simulations to help forecast and shape the future development of devices that might change the way arrhythmia is treated (e.g., via incorporation of new opsin variants like anion channelrhodopsins)
- Using optogenetics to modulate propensity for afterdepolarization-mediated ectopic beats
The lead trainee researcher in this area is Alex Ochs.
The lab’s most recent endeavor is to use artificial intelligence (AI) to pinpoint COVID-19 patients at risk for adverse cardiac outcomes using a single ECG recording acquired at hospital intake. Identifying these patients most in need of care will allow clinicians to monitor and provide treatment, while minimizing their own risk and eliminating unnecessary testing. AI has been used successfully in similar areas, such as identifying patients with atrial fibrillation using ECGs acquired in the absence of arrhythmia. Thus, we surmise that AI can be used to identify those with risk of life-threatening cardiovascular complications using COVID-19 intake ECGs. We recently secured pilot funding to support this work.
We are simultaneously exploring other opportunities within the space of AI-based analysis of patient data in cardiac electrophysiology. More to come in this space!
The lead trainee researcher in this area is Amber (Zih-Hua) Chen.