Welcome to the Hiesinger Lab
Cardiothoracic Surgery Research
The Hiesinger Lab is home to a collaborative research environment comprised of teams of clinicians, engineers, and scientists that work to create translatable solutions to clinically relevant cardiovascular problems. Led by Dr. William Hiesinger, the lab uses computational protein design, computational fluid dynamics, molecular biology, bio-medical devices, and machine learning methods to tackle key challenges in treating heart failure.
Protein Engineering for Heart Failure
The global burden of ischemic heart disease increased by almost 30% between 1990-2000, and is responsible for more than 13% of all deaths in the United States. Timely restoration of blood flow to the infarct region, by either percutaneous coronary interventions (PCI) or coronary artery bypass surgery significantly reduces the risk of death. Unfortunately, despite aggressive restoration of blood flow to the heart, high risk patients who survive a myocardial infarct are still likely to develop heart failure over time. The Hiesinger lab uses computational protein engineering methods to design supra-therapeutic peptides that activate the cardiac neo-vascularization pathways that intrinsically heal the heart after a heart attack, allowing us to treat and even prevent ischemic cardiomyopathy.
The Hiesinger lab is working on developing novel electrospun coatings for aortic endovascular stents that can be used in a wider pool of patients - including those with genetic aortic diseases. These electrospun coatings are custom designed to increase bio-compatibility and allow for a natural uptake of cells into the device.
Cardiovascular Computational Fluid Dynamics
The Hiesinger Lab is working in collaboration with the Marsden Lab, to develop methods to model blood flow through the left ventricle of the heart. These methods are being used to study hypertrophic obstructive cardiomyopathy or HOCM, and patients who develop aortic valve disease when on long term LVAD support. Our goal is to use computational fluid dynamics to develop a mechanistic understanding of disease processes that are difficult to study by traditional imaging modalities, and use this to optimize surgical technique.
Machine Learning for LVAD patients
Between 20-35% of all patients considered for LVADs develop a clinically significant degree of right ventricular failure (RV failure) after the procedure. The outcomes are significantly poorer in patients with severe post-operative RV failure than those without, with a 2.6 fold increase in the risk of death at 2 years. Multiple attempts have been made at developing predictive scoring algorithms for post-LVAD RV failure, but all have achieved only moderate success. Our lab is working on using artificial intelligence and advanced image segmentation techniques to predict the onset of right ventricular failure in LVAD patients. Grant funded by Stanford AI in Medicine Center.