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Principal Investigator: Judy Gichoya
Organization: MAYO CLINIC ARIZONA
Fiscal Year: 2024
Award: $698,520
Funding agency: National Heart Lung and Blood Institute
PROJECT SUMMARY
Atherosclerotic cardiovascular disease (ASCVD), such as stroke and heart attack, is the leading cause of
morbidity and mortality globally, responsible for approximately 19 million deaths annually. Risk assessment is
the cornerstone for primary prevention of ASCVD. Pooled Cohort Equations (PCE) are currently used to guide
risk assessment and tailor preventive therapies. However, these and other risk prediction tools remain imperfect
and have significant limitations including being static and based on a small number of simple clinical variables
as well as having poor performance across diverse populations. Moreover, they do not incorporate imaging that
may contain known prognostic biomarkers of future risk. For example, CT scans of the chest contain coronary
artery calcium (CAC), thoracic aortic calcium (TAC), intrathoracic (IF), and body composition (BC) metrics.
However, these biomarkers are not routinely reported in clinical practice nor accounted for in PCE. Finally, PCE
has also been shown to misestimate risk in certain ethnicities despite identical risk profiles. We will develop a
comprehensive graph-based fusion model, “ADMIRE” (AscvD Multimodal rIsk pREdiction), that incorporates
imaging and non-imaging data across two diverse sites – Mayo Clinic and Emory Health System. Our
multidisciplinary team of radiologists, informaticists, AI scientists, and preventive cardiologists will leverage our
prior experience with developing fusion models for risk prevention. We will validate our previously developed
biomarker segmentation models on a diverse cohort as well as develop a comprehensive semantic segmentation
model that incorporates multiple known prognostic biomarkers (AIM 1). We will then apply debiasing techniques
to develop ‘fair’ models and evaluate performance on cohorts stratified by demographic (e.g., race, gender,
social determinants of health surrogates) and imaging (e.g., scanner type) factors (AIM 2). Finally, we will use a
novel graph-based technique to create a fusion model to show performance against PCE and on stratified
cohorts based on demographics (AIM 3). We designed this study such that dependencies between experiments
are reduced. We hypothesize that multimodal fusion models incorporating imaging and non-imaging biomarkers
will have a greater prognostic performance than PCE. We further hypothesize that algorithmic model debiasing
can allow more effective at-risk prediction for minority patients in which PCE is known not to perform well. Our
proposal could potentially allow greater opportunistic screening of patients for primary prevention of ASCVD and
overcome limitations of current risk assessment tools such as PCE.
Terms: <21+ years old><Address><Adipose tissue><Adult><Adult Human><Algorithms><American><American Heart Association><Apoplexy><Assessment instrument><Assessment tool><Atherosclerosis><Atherosclerotic Cardiovascular Disease><BMI><BMI percentile><BMI z-score><Biological Markers><Black><Black race><Body Composition><Body mass index><Brain Vascular Accident><CAT scan><CT X Ray><CT Xray><CT imaging><CT scan><Calcium><Cardiac infarction><Cardiology><Cardiovascular><Cardiovascular Body System><Cardiovascular Organ System><Cardiovascular system><Categories><Cause of Death><Cerebral Stroke><Cerebrovascular Apoplexy><Cerebrovascular Stroke><Cessation of life><Chest><Clinic><Clinical><Clinical Data><Computed Tomography><Computerized Medical Record><Coronary><Data><Data Element><Death><Dedications><Dependence><Electronic Medical Record><Equation><Ethnic Origin><Ethnicity><Event><Fatty Tissue><Future><Gender><Goals><Graph><Health system><Heart Vascular><Image><Individual><Individuals from minority><Individuals of minority><Ischemic Heart><Ischemic Heart Disease><Ischemic myocardium><Manuals><Methods><Minority Groups><Minority People><Minority Population><Minority individual><Modeling><Morbidity><Morbidity - disease rate><Myocardial Infarct><Myocardial Infarction><Myocardial Ischemia><Nature><Obesity><Patients><Performance><Population><Population Heterogeneity><Predicting Risk><Preventative intervention><Preventative strategy><Preventative therapy><Prevention><Prevention strategy><Preventive><Preventive strategy><Preventive therapy><Primary Prevention><Prognostic Marker><Quetelet index><Race><Races><Recommendation><Reporting><Risk><Risk Assessment><Risk Estimate><Scanning><Scientist><Semantics><Site><Skeletal Muscle><Slice><Stroke><Techniques><Technology><Therapeutic><Thorace><Thoracic><Thoracic aorta><Thorax><Tomodensitometry><Training><United States><Universities><Validation><Visceral><Voluntary Muscle><Work><X-Ray CAT Scan><X-Ray Computed Tomography><X-Ray Computerized Tomography><Xray CAT scan><Xray Computed Tomography><Xray computerized tomography><adipose><adiposity><adulthood><atheromatosis><atherosclerotic disease><atherosclerotic vascular disease><bio-markers><biologic marker><biomarker><brain attack><cardiac infarct><cardiac ischemia><cardiovascular disease risk><cardiovascular disorder risk><catscan><cerebral vascular accident><cerebrovascular accident><chest CT><chest computed tomography><circulatory system><clinical practice><cohort><college><collegiate><computed axial tomography><computer tomography><computerized axial tomography><computerized tomography><coronary artery calcium><coronary attack><coronary calcium><coronary infarct><coronary infarction><coronary ischemia><corpulence><deep learning><deep learning based model><deep learning method><deep learning model><deep learning strategy><demographics><design><designing><diverse populations><experience><experiment><experimental research><experimental study><experiments><falls><forecasting risk><heart attack><heart infarct><heart infarction><heart ischemia><heterogeneous population><imaging><imaging Segmentation><imaging biomarker><imaging marker><imaging-based biological marker><imaging-based biomarker><imaging-based marker><insurance plan><intervention for prevention><mid life><mid-life><middle age><middle aged><midlife><minority patient><mortality><multi-modality><multidisciplinary><multimodal data fusion><multimodal fusion><multimodality><muscle bulk><muscle form><muscle mass><myocardial ischemia/hypoxia><myocardium ischemia><non-contrast CT><noncontrast CT><noncontrast computed tomography><novel><patient population><patient screening><patients from minority><patients of minority><population diversity><predict risk><predict risks><predicted risk><predicted risks><predicting risks><predictive risk><predictive tools><predicts risk><prevention intervention><preventional intervention strategy><preventive intervention><prognostic><prognostic biomarker><prognostic performance><race bias><racial><racial background><racial bias><racial origin><radiologist><risk prediction><risk prediction algorithm><risk prediction model><risk predictions><risk stratification><screening><screenings><social health determinants><stratify risk><stroked><strokes><subcutaneous><subdermal><tool><validations><white adipose tissue><yellow adipose tissue>