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Principal Investigator: CHRISTINE M ALBERT
Organization: CEDARS-SINAI MEDICAL CENTER
Fiscal Year: 2024
Award: $1,491,021
Funding agency: National Heart Lung and Blood Institute
Sudden and/or arrhythmic death (SAD), which typically results from lethal ventricular arrhythmias (ventricular
tachycardia and ventricular fibrillation, VT/VF) in the setting of coronary heart disease (CHD), afflicts an
estimated 310,000 persons annually in the United States. Reductions in SAD have continued to lag those
observed for other coronary heart disease (CHD) outcomes despite advances in resuscitation therapies and
the use of implantable cardioverter-defibrillators (ICDs). Current approaches to SAD prevention remain
centered on placing ICDs in patients with left ventricular ejection fraction (LVEF) <30-35% – even though the
majority of SAD occurs in the setting of LVEF >30-35%. In effect, the proportionately larger segment of the
at-risk population has been understudied and thus undertreated. Despite this unmet need, there remain very
few, if any, prospective studies examining SAD risk prediction in individuals with CHD and LVEF >30-35% over
a long enough time horizon where ICD therapy might be cost-effective. For this very reason, the PRE-
DETERMINE Cohort Study was intentionally designed to address this scientific gap and prospectively
study clinically relevant approaches to SAD risk prediction in CHD patients with LVEF >30-35%. In this
application, we propose to leverage the originally NHLBI-funded base cohort resource to continue adjudication
of accruing SAD and VT/VF events, in addition to competing causes of death, to attain 10+ years of endpoint
adjudication to enable the development and validation of multi-marker SAD risk prediction models based on
combinations of multi-dimensional clinical, ECG, imaging, biomarker, and genetic data generated in this unique
multicenter cohort of 5761 CHD patients. We will also leverage the base cohort to interrogate novel fatty acid
derived eicosanoids and putative arrhythmia modulating proteomic analytes in relation to risk for SAD and
competing causes of mortality in patients with CHD. Novel methods of competing risk analyses will be used to
integrate absolute and proportional SAD risk into SAD risk prediction models and to elucidate separate
associations between SAD vs. non-SAD causes of death. Machine learning approaches will be applied to
uncover inter-relations and latent features from multi-modality data not easily detected by conventional models.
An overarching goal of our work is to accurately identify those individual subsets of the broader population who
have sufficiently high absolute and proportional risk for SAD that they warrant inclusion in randomized trials of
primary prevention ICD therapy. The aims of the current proposal also offer new opportunities to identify
potential mechanistic pathways underlying the genesis of lethal ventricular arrhythmias that could serve as
novel targets for SAD prevention – extending beyond ICD placement – in patients with CHD and possibly even
in the general population wherein CHD underlies most SAD events. The continuation and expansion of the
PRE-DETERMINE study will provide the scientific field with a one-of-a kind resource for investigators and
trainees collaborating toward the common goal of reducing the burden of SAD.
Terms: <Accounting><Active Follow-up><Address><Area><Arrhythmia><Assay><Bioassay><Biological Assay><Biological Markers><Blood Platelets><Blood Tests><Cardiac><Cardiac Arrhythmia><Cardiac Death><Cardiac Diseases><Cardiac Disorders><Cause of Death><Cessation of life><Clinical><Cohort Studies><Collaborations><Concurrent Studies><Coronary Arteriosclerosis><Coronary Artery Disease><Coronary Artery Disorder><Coronary Atherosclerosis><Coronary Disease><Coronary heart disease><Data><Death><Development><Dimensions><Disease Outcome><Dysfunction><ECG><EKG><Eicosanoids><Electrocardiogram><Electrocardiography><Event><Fatty Acids><Functional disorder><Funding><General Population><General Public><Genetic><Genotype><Goals><Groups at risk><Heart Arrhythmias><Heart Diseases><Hematologic Tests><Hematological Tests><Hematology Testing><Image><Implantable Cardioverter-Defibrillators><Implantable Defibrillators><Individual><Inflammatory><Infrastructure><Intervention><Intervention Strategies><Investigators><Investments><Knowledge><LVEF><Left Ventricular Ejection Fraction><Life><Life Expectancy><Machine Learning><Marrow platelet><Methods><Modeling><Molecular Fingerprinting><Molecular Profiling><NHLBI><National Heart, Lung, and Blood Institute><Pathway interactions><Patients><People at risk><Persons><Persons at risk><Phenotype><Physiopathology><Platelets><Population><Populations at Risk><Position><Positioning Attribute><Predicting Risk><Prevention><Prevention Research><Primary Prevention><Procedures><Prospective Studies><Prospective, cohort study><Proteins><Proteomics><Public Health><Randomization trial><Research Personnel><Research Resources><Researchers><Resources><Resuscitation><Risk><Risk Marker><Sudden Death><Therapeutic><Thrombocytes><Time><United States><Validation><Ventricular Arrhythmia><Ventricular Fibrillation><Ventricular Tachycardia><Work><active followup><adjudication><adjudicative process and procedure><advanced analytics><atherosclerotic coronary disease><atherosclerotic heart disease><base><bases><bio-markers><biologic marker><biomarker><cardiac damage><cardiac rhythm><clinical relevance><clinically relevant><cohort><computer based prediction><coronary arterial disease><coronary disorder><cost effective><cost efficient><death risk><design><designing><developmental><follow up><follow-up><followed up><followup><forecasting risk><genome scale><genome-wide><genomewide><heart damage><heart disorder><heart rhythm><high risk><imaging><improved><innovate><innovation><innovative><insight><interventional strategy><machine based learning><machine learning based method><machine learning method><machine learning methodologies><molecular profile><molecular signature><mortality><mortality risk><multi-modal data><multi-modal datasets><multi-modality><multimodal data><multimodal datasets><multimodality><neglect><new approaches><novel><novel approaches><novel strategies><novel strategy><pathophysiology><pathway><predict risk><predict risks><predicted risk><predicted risks><predicting risks><predictive modeling><predictive risk><predicts risk><prevent><preventing><randomized trial><retention rate><retention strategy><risk prediction><risk prediction algorithm><risk prediction model><risk predictions><risk predictor><risk predictors><risk stratification><stratify risk><tool><validations>