Project Grant R01HL166508
- The National Heart, Lung, and Blood Institute (NHLBI) awarded a $429,153 Project Grant under the Cardiovascular Diseases Research program (CFDA 93.837) to Michigan Technological University. The funding supports research to improve patient selection and left ventricular lead placement for cardiac resynchronization therapy (CRT) by integrating analysis of electrical dyssynchrony on ECG and mechanical dyssynchrony on gated SPECT myocardial perfusion imaging. The project aims to use unsupervised...
- This $437,461 federal Project Grant award from the National Heart, Lung, and Blood Institute (NHLBI), funded under the Cardiovascular Diseases Research program (CFDA 93.837), supports the development of a novel stabilization catheter device for His pacing lead implantation. The goal is to improve the accuracy and ease of His bundle pacing, an emerging alternative cardiac resynchronization therapy (CRT) for heart failure patients. The project aims to refine a deployable suction-tipped lead...
- This $783,801 Project Grant, awarded by the National Heart, Lung, and Blood Institute (NHLBI) under the Cardiovascular Diseases Research program (CFDA 93.837) on September 3, 2025, supports research to demonstrate the feasibility of democratizing cardiac magnetic resonance imaging (CMR) technology for use in community family health centers. The award, which extends through May 31, 2029, funds development and implementation of an automated, push-button CMR protocol (AutoCMR) combined with...
- This $760,046 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of a computationally-efficient multiscale modeling framework that integrates machine learning and artificial intelligence to predict structural and functional changes in the heart due to disease progression. The project aims to build fundamental understanding of heart disease by combining techniques from...
- This federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $439,954 to Michigan State University (MSU) to develop advanced computational models of the heart that leverage machine learning and artificial intelligence. The goal is to create a multiscale modeling framework that can predict structural and functional changes in the heart due to disease conditions like pathological fibrosis. The...
- This Project Grant award from the National Science Foundation (NSF) under the Engineering program (CFDA 47.041) provides $199,992 to Boston University to develop a high-throughput electrical stimulation screening platform for assessing the long-term effects of cardiac contractility modulation (CCM) devices on engineered human heart tissue. The project will support a postdoctoral scholar co-mentored by researchers at the Food and Drug Administration (FDA) and Boston University to advance...
- This Project Grant award from the National Heart, Lung, and Blood Institute (NHLBI), under the Cardiovascular Diseases Research federal grant program (CFDA 93.837), will support a $704,611 research project to develop and validate an AI-based model, named EchoNext, that can accurately detect undiagnosed structural heart disease (SHD) from electrocardiogram (ECG) data. The project aims to: 1) prospectively validate the accuracy of EchoNext in detecting undiagnosed SHD in patients presenting to...
- This Project Grant award from the National Heart, Lung, and Blood Institute (NHLBI), under the Cardiovascular Diseases Research program (CFDA 93.837), provides $742,395 to Case Western Reserve University (CWRU) to develop and validate a machine learning-based analysis of coronary artery calcium scans (CTCS) to identify biomarkers for predicting heart failure risk. The 4-year project aims to create an automated tool for extracting CTCS-derived radiomics, develop a comprehensive heart failure risk...
- This $200,000 Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CFDA 47.070) program will fund the development of advanced computational methods to create high-fidelity, fast-running digital twins of patient hearts and cardiovascular medical devices. The project aims to deliver: 1) novel machine learning algorithms for accurate digital twin geometry reconstruction from 3D medical images, 2) an efficient inverse method to identify in vivo...
- Federal Project Grant Award Summary Tufts Medical Center received a $793,868 Project Grant from the National Heart, Lung, and Blood Institute (NHLBI) under the Cardiovascular Diseases Research program (CFDA 93.837), awarded August 1, 2025, with a completion date of May 31, 2030. The award supports the development and validation of machine learning (ML) methods to automate the diagnosis of aortic stenosis (AS) from cardiac ultrasound imaging. Aortic stenosis affects over 12.6 million adults in...
MATHEMATICAL MODEL-BASED OPTIMIZATION OF CRT RESPONSE IN ISCHEMIA - PROJECT SUMMARY/ABSTRACT APPLICATION OF MULTISCALE COMPUTER MODELING TO HELP GUIDE AND ELUCIDATE HEART DISEASE TREATMENTS IS EMERGING. COMPUTATIONAL MODELING, HOWEVER, HAS NOT BEEN EXPLOITED FOR OPTIMIZING CARDIAC RESYNCHRONIZATION THERAPY (CRT). WHILE CRT HAS EMERGED AS A POWERFUL TREATMENT FOR HEART FAILURE (HF) TO RESTORE NORMAL ACTIVATION PATTERN IN THE HEART, ABOUT 30% OF PATIENTS STILL DO NOT IMPROVE AFTER THERAPY (NON-RESPONDERS). IMPROVEMENT OF RESPONDER RATE THEREFORE REMAINS A CRUCIAL CLINICAL CHALLENGE AND THE HOLY GRAIL OF CRT. WE BELIEVE THAT COMPUTATIONAL MODELING CAN HELP OPTIMIZE CRT AND IMPROVE THE RESPONDER RATE. EQUALLY IMPORTANT, THE DEVELOPMENT OF A MULTISCALE COMPUTATIONAL FRAMEWORK THAT CONSIDERS THE KEY PHYSICS OF THE HEART CAN HELP UNDERSTAND SEVERAL NOVEL PACING THERAPIES (E.G., CONDUCTION SYSTEM PACING (CSP) INCLUDING HIS BUNDLE PACING AND LEFT BRANCH BUNDLE (LBB) PACING) THAT HAVE BEEN DEVELOPED RECENTLY TO IMPROVE THE RESPONDER RATE. SPECIFICALLY, COMPUTATIONAL MODELING CAN HELP ELUCIDATE THE KEY FACTORS AFFECTING THE LONG AND SHORT-TERM EFFECTIVENESS OF THESE PACING THERAPIES IN PATIENTS WITH DIFFERENT INTRAVENTRICULAR CONDUCTION DELAY AND/OR LV SCAR/ISCHEMIA. HERE, THE OVERALL GOAL HERE IS TO DEVELOP COMPUTATIONAL APPROACHES THAT COMBINE MACHINE LEARNING ALGORITHMS AND PHYSICS-BASED MODELING TO FUNDAMENTALLY UNDERSTAND THE SHORT AND LONG-TERM EFFECTS OF CRT THAT INCLUDES CSP, OPTIMIZE CRT, AND TO ELUCIDATE THE ADVANTAGES AND DISADVANTAGES OF CSP OVER STANDARD CRT. THE FOLLOWING SPECIFIC AIMS ARE CONSTRUCTED TO ACCOMPLISH THIS GOAL. FIRST, WE WILL DEVELOP AN EXPERIMENTALLY-VALIDATED MULTISCALE CARDIAC ELECTRO-MECHANICS-PERFUSION (EMP) COMPUTATIONAL FRAMEWORK TO SIMULATE THE CHRONIC EFFECTS OF CRT AND CSP IN TREATING MECHANICAL DYSSYNCHRONY IN LBBB + ISCHEMIA. SECOND, WE WILL INTEGRATE THE COMPUTATIONAL MODELING FRAMEWORK WITH EFFICIENT MACHINE LEARNING AND OPTIMIZATION ALGORITHMS TO OPTIMIZE CRT WITH LV EPICARDIAL AND ENDOCARDIAL PACING IN ISCHEMIA. THIRD, WE WILL USE THE VALIDATED MULTISCALE COMPUTATIONAL EMP FRAMEWORK TO ELUCIDATE THE EFFECTS AND FACTORS AFFECTING THE RESPONSE OF CSP IN ISCHEMIA. THE PROPOSED APPROACH AND METHODOLOGIES ARE INNOVATIVE. MORE IMPORTANTLY, SUCCESSFUL COMPLETION WILL DIRECTLY TRANSLATE THE FINDINGS TO THE CLINIC FOR OPTIMIZATION OF CRT THERAPY TO REDUCE NON-RESPONDER RATES AS WELL AS PATIENT IDENTIFICATION FOR DIFFERENT PACING THERAPIES. THIS WOULD HAVE SUBSTANTIAL IMPACT ON IMPROVING THE TREATMENT AND REDUCING THE COST OF HF EPIDEMIC.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $771.2k | 7/25/25 | ||
| Not listed | $765.1k | 6/28/24 | ||
| Not listed | $812.0k | 6/5/23 | ||
| Not listed | $812.0k | 6/5/23 |