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.

项目概要肥厚型心肌病是最常见的遗传性心肌病，估计有 750,000 在美国受影响的个人。然而，只有约10万人被确诊，这表明对于患有临床前或明显疾病的个体来说，也存在显着的诊断和治疗差距至于他们的高危家庭成员。因此，确定应接受评估的个人非常重要在出现包括心脏在内的高度发病结果之前进行早期诊断和针对性治疗衰竭、心律失常、中风和猝死。电子健康记录提供了高维度、纵向表型信息可用于创建更灵敏和特异性的诊断算法。在这项以患者为导向的职业发展奖提案中，Nosheen Reza 博士的目标是通过创建和评估来提高识别肥厚型心肌病患者的能力机器学习分类模型利用来自不同来源的电子健康记录数据人口。在目标 1 中，她将推导并验证基于多机构心电图的模型使用 Penn Medicine 电子健康记录中的数据检测肥厚型心肌病，并将评估在该模型中添加额外的电子健康记录衍生特征是否可以改善模型检测肥厚型心肌病患者的能力。在目标 2 中，她将从外部验证最好的在两个大型独立卫生系统中执行电子健康记录派生模型。在目标 3 中，她将使用实施科学方法来确定临床医生特定的障碍和促进因素，以实现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设备的态度健康记录衍生的肥厚型心肌病诊断模型。总的来说，这些目标将导致可推广的电子健康记录的前瞻性传播和实施研究诊断工具，以促进肥厚型心肌病患者的早期识别和风险分层。 Reza 博士是一位早期职业研究者、遗传性和晚期心力衰竭心脏病专家，他有一个长期目标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精准医疗护理患者的工具患有心肌病和心力衰竭。该 K23 奖项将支持 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。礼萨将接受宾夕法尼亚大学优秀的导师和顾问团队的监督由遗传性心肌病、电子健康记录研究领域的国家领导者组成翻译生物信息学。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。