Advanced simulations of cardiovascular hemodynamics and physiology are now being incorporated into clinical decision-making, surgical planning, and the FDA approval process. Simulations have potential to influence life- altering decisions for patients. As a result, these advancements come with an ever-increasing responsibility to the patients and the clinicians who treat them to prove that simulations produce reliable and safe results. It is dangerous and irresponsible for the simulation community to continue to push for routine clinical use of patient-specific multiscale models without providing a means to statistically quantify the reliability of their predictions. Development of transformative technology to assess uncertainty, which is currently lacking, will mitigate patient risk and ultimately enable safe and routine use of simulations for personalized medicine. Patient specific cardiovascular (CV) simulations require a combination of uncertain assumptions and inputs from clinical and imaging data. This issue currently gets swept under the rug, asking end-users to accept deterministic simulation predictions as truth with no associated confidence intervals. This leads to justified skepticism in the clinical community regarding the trustworthiness of simulations, and is a roadblock to clinical use and eventual FDA approval. We propose to address this unmet need by creating a suite of efficient and automated uncertainty quantification (UQ) tools to assess and improve the reliability of patient-specific simulation predictions. We wil establish our UQ framework through application to multiscale simulations of coronary artery disease (CAD). Coronary modeling is an ideal test-bed and challenge for UQ methodologies, with multi-parameter uncertainty arising from image segmentation, material properties, and complex physiology. To accomplish our objectives, we propose three specific aims: 1) An integrative multi- modality imaging study that will increase model fidelity and enable uncertainty assessment, 2) Creation of automated parameter-estimation tools for assimilation of clinical data into cardiovascular simulations, and 3) Development of an efficient computational framework to quantify uncertainties in simulations of CAD. The proposed work is significant because we will (1) raise the bar for the CV simulation community to report output statistics, (2) establish standards for adoption of simulations in clinical care and by other researchers, and (3) provide a novel suite of tools through the open-source SimVascular project. It is innovative because (1) UQ is performed to establish confidence intervals on simulation outputs and (2) the myriad uncertainties typically un- discussed in the CV simulation community are rigorously quantified. Our multi-disciplinary team consists of investigators with expertise in patient-specifi modeling, mathematical methods for UQ, high-performance computing, and medical imaging. We have a strong track record of joint publication, clinical translation, and funded collaborations Our translational goal is to provide the cardiovascular simulation community with efficient tools for UQ, raising the bar for simulation reliability and ultimately increasing clinical adoption.
Cardiovascular simulations offer non-invasive means to perform surgical planning and risk stratification in adult and pediatric heart patients, with increasing clinical application. These advancements come with an ever- increasing responsibility to the patients and the clinicians who treat them to prove that simulations produce reliable and safe results, given the myriad uncertainties in simulation inputs. This proposal will bring about a paradigm shift in the patient specific modeling community by producing the first quantifiable statistics on simulation predictions through the introduction of a novel suite of uncertainty quantification tools, made available through the SimVascular open source project.
