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Improving the Detection of Hypertrophic Cardiomyopathy Using Machine Learning Applied to Electronic Health Record Data

利用机器学习应用于电子健康记录数据来改善肥厚型心肌病的检测

基本信息

批准号：
10740278
负责人：
Nosheen Reza
金额：
$ 17.33万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-01 至 2028-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10740278
关键词：
Academic Medical Centers Address Adverse event Advisory Committees Affect Arrhythmia Attitude Bioinformatics Calibration Cardiomyopathies Cardiovascular Diseases Cardiovascular system Classification Clinical Clinical Informatics Code Complex Consolidated Framework for Implementation Research Coupled Data Data Science Data Scientist Data Set Derivation procedure Detection Development Development Plans Diagnosis Diagnostic Diagnostic Imaging Discrimination Disease Disease Surveillance Dissemination and Implementation EHR research Early Diagnosis Electrocardiogram Electronic Health Record Evaluation Family member Funding Genetic Goals Health system Heart failure Hypertension Hypertrophic Cardiomyopathy Individual Inherited Institution Interview K-Series Research Career Programs Laboratories Left Ventricular Hypertrophy Machine Learning Medicine Mentored Patient-Oriented Research Career Development Award Mentors Mentorship Methods Modality Modeling Outcome Patient Care Patients Pennsylvania Performance Persons Phenotype Population Population Heterogeneity Publishing Research Research Personnel Risk Site Source Stroke Sudden Death Symptoms Therapeutic Training United States Universities Validation accurate diagnosis biomedical referral center black patient cardiac muscle disease care delivery career career development clinical phenotype clinically relevant cohort comorbidity cost diagnostic algorithm diagnostic criteria diagnostic tool diverse data future implementation genomic data hands-on learning high dimensionality implementation science implementation strategy implementation study improved informatics tool inherited cardiomyopathy machine learning classification machine learning model machine learning prediction multimodality patient oriented patient population portability pre-clinical precision medicine predictive modeling prevent prospective research and development risk mitigation risk stratification targeted treatment trait

项目摘要

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.

项目摘要肥厚型心肌病是最常见的遗传性心肌疾病，在美国受影响的人。然而，只有大约10万人被确诊，这表明对于患有临床前或明显疾病的个体，至于他们有危险的家庭成员。因此，必须确定应接受评估的个人在出现包括心脏病在内的高度病态结局之前，衰竭心律失常中风和猝死电子健康记录提供了一个高维度的来源，纵向表型信息，可用于创建更敏感和特异性的诊断算法在这个以病人为导向的指导职业发展奖的建议，博士Nosheen礼萨的目的是通过创建和评估来提高识别肥大性心肌病患者的能力机器学习分类模型，利用电子健康记录数据，人口。在目标1中，她将推导并验证多机构心电图模型，使用宾夕法尼亚医学电子健康记录的数据检测肥厚性心肌病，评估是否增加额外的电子健康记录衍生的性状，以这个模型改善模型检测肥厚型心肌病患者的能力。在目标2中，她将从外部验证最佳方案在两个大型独立的卫生系统中执行电子健康记录衍生模型。在目标3中，她将使用实施科学的方法，以确定临床医生的具体障碍和促进者的准确和及时诊断肥厚型心肌病，并评估临床医生对使用电子基于健康记录肥厚型心肌病诊断模型综合起来，这些目标将导致一个可推广的电子健康记录衍生的前瞻性传播和实施研究诊断工具，以促进早期识别和风险分层的个人与肥厚型心肌病。博士Reza是一名早期职业调查员，也是一名遗传和高级心力衰竭心脏病专家，成为一名独立资助的心血管数据科学家，专注于应用临床信息学利用电子健康记录和基因组数据实现患者护理中的精准医疗的工具患有心肌病和心力衰竭K23奖将支持Reza博士通过以下方式实现这一目标：生物信息学、机器学习和实施科学方面全面而严格的培训计划。博士 Reza将由宾夕法尼亚大学的优秀导师和咨询团队监督包括遗传性心肌病，电子健康记录为基础的研究，翻译生物信息学本提案中概述的指导研究和职业发展计划将指导Reza博士过渡到独立资助的研究生涯。