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

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

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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.

项目摘要肥厚性心肌病是最常见的遗传性心肌疾病，估计为750,000 在美国影响个人。但是，只有大约100,000人被诊断出对于临床前或公开疾病的个体也存在明显的诊断和治疗差距至于他们的高危家庭成员。因此，重要的是要确定应该接受评估的个人对于早期的诊断和有针对性的治疗，在发展高度病态的结果之前失败，心律不齐，中风和猝死。电子健康记录提供了高维的来源可以利用的纵向表型信息，以创建更敏感和特定的诊断算法。在这项以患者为导向的指导职业发展奖提案中，Nosheen Reza博士的目标是通过创建和评估来提高识别肥厚心肌病的人的能力机器学习分类模型，利用电子健康记录数据来自多样的数据人群。在AIM 1中，她将得出并验证一个基于心电图的多机构的模型使用Penn Medicine Electronic Health Record中的数据检测肥厚的心肌病，并将评估在该模型中添加其他电子健康记录衍生的特征是否可以改善模型检测肥厚性心肌病患者的能力。在AIM 2中，她将在外部验证最好的在两个大型独立卫生系统中执行电子健康记录的模型。在AIM 3中，她会使用实施科学方法来识别准确和促进者的临床医生特定障碍和促进者及时诊断肥厚的心肌病并评估临床医生对使用电子的态度健康记录的肥厚性心肌病的诊断模型。综上所述，这些目标将导致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可促进肥厚性心肌病个体的早期识别和风险分层。早期职业调查员，遗传和高级心力衰竭心脏病专家Reza博士的长期目标成为一名独立资助的心血管数据科学家，重点是应用临床信息学利用电子健康记录和基因组数据来实现患者护理的精确药物的工具患有心肌病和心力衰竭。该K23奖将支持Reza博士通过生物信息学，机器学习和实施科学方面的全面，严格的培训计划。博士 REZA将由宾夕法尼亚大学的杰出指导和咨询团队监督由国家遗传性心肌病，基于电子健康记录的研究和翻译生物信息学。该提案中概述的指导研究和职业发展计划将指导Reza博士向独立资助的研究职业过渡。