Document text
Principal Investigator: Nosheen Reza
Organization: UNIVERSITY OF PENNSYLVANIA
Fiscal Year: 2024
Award: $172,832
Funding agency: National Heart Lung and Blood Institute
PROJECT SUMMARY
Hypertrophic cardiomyopathy is the most common inherited cardiac muscle disease with an estimated 750,000
affected individuals in the United States. However, only about 100,000 people have been diagnosed, suggesting
that there are significant diagnostic and treatment gaps for individuals with pre-clinical or overt disease, as well
as for their at-risk family members. Therefore, it is important to identify individuals who should undergo evaluation
for earlier diagnosis and targeted treatment, prior to the development of highly morbid outcomes including heart
failure, arrhythmias, stroke, and sudden death. The electronic health record offers a source of high dimensional,
longitudinal phenotype information that can be leveraged to create more sensitive and specific diagnostic
algorithms. In this patient-oriented mentored career development award proposal, Dr. Nosheen Reza aims to
improve the ability to identify individuals with hypertrophic cardiomyopathy through creation and evaluation of
machine learning classification models that leverage electronic health record data derived from diverse
populations. In Aim 1, she will derive and validate a multi-institutional electrocardiogram-based model for the
detection of hypertrophic cardiomyopathy using data from the Penn Medicine electronic health record and will
evaluate whether the addition of additional electronic health record-derived traits to this model improves the
model's ability to detect patients with hypertrophic cardiomyopathy. In Aim 2, she will externally validate the best
performing electronic health record-derived models in two large independent health systems. In Aim 3, she will
use implementation science methods to identify clinician-specific barriers to and facilitators of accurate and
timely diagnosis of hypertrophic cardiomyopathy and assess clinicians' attitudes toward the use of an electronic
health record-derived diagnostic model for hypertrophic cardiomyopathy. Taken together, these aims will lead to
prospective dissemination and implementation studies of a generalizable electronic health record-derived
diagnostic tool to facilitate early recognition and risk stratification of individuals with hypertrophic cardiomyopathy.
Dr. Reza, an early career investigator and genetic and advanced heart failure cardiologist, has a long-term goal
of becoming an independently funded cardiovascular data scientist with a focus on applying clinical informatics
tools that leverage electronic health record and genomic data to enable precision medicine in the care of patients
with cardiomyopathy and heart failure. This K23 award will support Dr. Reza in achieving this goal through a
comprehensive and rigorous training plan in bioinformatics, machine learning, and implementation science. Dr.
Reza will be supervised by an outstanding mentorship and advisory team at the University of Pennsylvania
consisting of national leaders in genetic cardiomyopathies, electronic health record-based research, and
translational bioinformatics. The mentored research and career development plan outlined in this proposal will
guide Dr. Reza's transition to an independently funded research career.
Terms: <Academic Medical Centers><Address><Adverse Experience><Adverse event><Advisory Committees><Affect><Apoplexy><Arrhythmia><Asymmetric Septal Hypertrophy><Attitude><Bio-Informatics><Bioinformatics><Brain Vascular Accident><Calibration><Cardiac Arrhythmia><Cardiomyopathies><Cardiovascular><Cardiovascular Body System><Cardiovascular Diseases><Cardiovascular Organ System><Cardiovascular system><Career Development Awards><Career Development Awards and Programs><Career Development Programs K-Series><Cerebral Stroke><Cerebrovascular Apoplexy><Cerebrovascular Stroke><Classification><Clinical><Clinical Informatics><Code><Coding System><Cognitive Discrimination><Complex><Consolidated Framework for Implementation Research><Consolidated Framework for Implementation Science><Consolidated Framework for Implementing Change><Coupled><Data><Data Science><Data Scientist><Data Set><Derivation><Derivation procedure><Detection><Development><Development Plans><Development and Research><Diagnosis><Diagnostic><Diagnostic Imaging><Discrimination><Disease><Disease Surveillance><Disorder><Dissemination and Implementation><ECG><EHR based research><EHR research><EKG><Early Diagnosis><Electrocardiogram><Electrocardiography><Electronic Health Record><Evaluation><Family member><Funding><Genetic><Goals><Health system><Heart Arrhythmias><Heart Vascular><Heart failure><Hereditary><Hereditary ventricular hypertrophy><Hypertension><Hypertrophic Cardiomyopathy><Hypertrophic Obstructive Cardiomyopathy><Idiopathic Hypertrophic Subvalvular Stenosis><Idiopathic hypertrophic subaortic stenosis><Individual><Inherited><Institution><Interview><Investigators><K-Awards><K-Series Research Career Programs><K23 Award><K23 Mechanism><K23 Program><Laboratories><Left Ventricular Hypertrophy><Machine Learning><Medicine><Mentored Patient-Oriented Research Career Development Award><Mentored Patient-Oriented Research Career Development Award (K23)><Mentors><Mentorship><Methods><Modality><Modeling><Myocardial Diseases><Myocardial Disorder><Myocardiopathies><Outcome><Patient Care><Patient Care Delivery><Patients><Pennsylvania><Performance><Persons><Phenotype><Population><Population Heterogeneity><Publishing><R & D><R&D><Research><Research Career Program><Research Personnel><Researchers><Risk><Site><Source><Stroke><Sudden Death><Symptoms><Systematics><Task Forces><Therapeutic><Training><United States><Universities><University Medical Centers><Validation><Vascular Hypertensive Disease><Vascular Hypertensive Disorder><accurate diagnosis><advisory team><applied learning><attributable risk><biomedical referral center><black patient><brain attack><cardiac failure><cardiac muscle disease><cardiovascular disorder><care delivery><care for patients><care of patients><career><career development><caring for patients><cerebral vascular accident><cerebrovascular accident><circulatory system><clinical phenotype><clinical relevance><clinically relevant><co-morbid><co-morbidity><cohort><comorbidity><computer based prediction><cost><data diversity><developmental><diagnostic algorithm><diagnostic criteria><diagnostic tool><diverse data><diverse populations><early detection><electronic health care record><electronic health medical record><electronic health plan record><electronic health record based research><electronic health registry><electronic medical health record><familial cardiomyopathy><future implementation><genetic cardiomyopathy><genomic data><genomic data-set><genomic dataset><hands-on learning><heart muscle disease><hereditary cardiomyopathy><heterogeneous population><high blood pressure><high dimensionality><hyperpiesia><hyperpiesis><hypertensive disease><hypertensive disorder><hypertrophic myocardiopathy><implementation science><implementation strategy><implementation study><improved><informatics tool><inherited cardiomyopathy><interactive engagement><interactive learning><machine based learning><machine learning based classification><machine learning based model><machine learning based prediction model><machine learning based predictive model><machine learning classification><machine learning model><machine learning prediction><machine learning prediction model><model generalizability><multi-modality><multimodality><myocardium disease><myocardium disorder><patient centered><patient oriented><patient population><population diversity><portability><pre-clinical><precision medicine><precision-based medicine><preclinical><predictive modeling><prevent><preventing><prospective><referral center><research and development><risk mitigation><risk stratification><strategies for implementation><stratify risk><stroked><strokes><targeted drug therapy><targeted drug treatments><targeted therapeutic><targeted therapeutic agents><targeted therapy><targeted treatment><trait><validations>