Accelerating research to advance care for adults with congenital heart disease through development of validated scalable computational phenotypes

通过开发经过验证的可扩展计算表型，加速研究以推进对患有先天性心脏病的成人的护理

• 批准号: 10214688
• 负责人: Alexander R. Opotowsky
• 金额: $ 73.84万
• 依托单位: CINCINNATI CHILDRENS HOSP MED CTR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-10 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10214688
• 关键词: Academic Medical Centers; Address; Adult; Algorithms; Architecture; Arrhythmia; Atrial Heart Septal Defects; Boston; Cardiac; Cardiovascular Diseases; Cardiovascular system; Caring; Cessation of life; Characteristics; Child; Chronic; Chronic Disease; Classification; Clinical; Code; Collaborations; Complex; Computerized Medical Record; Data; Data Set; Data Sources; Development; Diagnosis; Diagnostic Specificity; Disease; Eisenmenger Complex; Foundations; Functional disorder; Goals; Health; Healthcare; Heart; Heart failure; Heterogeneity; Hospitals; ICD-9; Incidence; Infrastructure; Intervention; Investigation; Label; Life; Lung; Manuals; Medical; Medical Informatics; Medical center; Methods; Modeling; Natural Language Processing; New York; Nomenclature; Operative Surgical Procedures; Outcome; Patient Care; Patient Monitoring; Patients; Pediatric Hospitals; Performance; Phenotype; Play; Population; Population Research; Positioning Attribute; Prognosis; Public Health; Publishing; Regimen; Research; Research Support; Resources; Risk; Role; Stroke; Testing; Text; Thromboembolism; Time; Training; Transposition of Great Vessels; Underserved Population; United States; Validation; Vascular Diseases; Visit; Woman; Work; adjudicate; administrative database; adverse outcome; base; biobank; clinical care; clinical data repository; clinical database; clinical decision support; clinical phenotype; clinically actionable; cohort; comorbidity; computable phenotypes; congenital heart disorder; data resource; design; disease diagnosis; evidence base; experience; high risk; improved; infancy; innovation; large scale data; large-scale database; mortality; multitask; neural network; neural network classifier; novel; palliation; patient population; population based; predict clinical outcome; predictive modeling; premature; prospective; repaired; risk prediction model; structured data; tool; treatment strategy

PROJECT SUMMARY The advent of surgery to treat congenital heart disease (CHD) in the second half of the 20th century shifted the care paradigm from palliation of disease fatal in infancy to management of lifelong chronic disease through adulthood. There are now more than 1.5 million adults with CHD living in the United States. These patients have a substantial burden of cardiovascular and other medical comorbidities, as well as markedly increased risk for adverse outcomes such as arrhythmia, heart failure, cerebrovascular accident, and premature death. The emergence of this population requires new clinical care models as well as the development of novel research tools and infrastructures to address these patients' unique characteristics and healthcare needs. Adult CHD is characterized by substantial complexity, era-dependent heterogeneity in treatment strategies, and time-varying implications of lifelong disease. This burgeoning population is understudied, and the pathophysiology of the component diseases remains incompletely understood. Billing and other administrative codes available in the electronic medical record are neither sensitive nor specific for CHD diagnosis and do not adequately describe many other salient clinical features. As a result, structured data in large administrative databases are not well suited to studying adults with CHD, even when the goal is simply to identify a cohort of patients with a given diagnosis. This constitutes a major impediment to research efforts and is the primary barrier underlying the limited population-based research performed to date. Adult CHD investigation would benefit immensely from methods to establish harmonized, large-scale, multi-center datasets. While billing codes are inadequate, the information needed to accurately classify adults with CHD is already available in the electronic medical record in the form of clinical notes, comprised mainly of unstructured (“free”) text. Manual data extraction is laborious, resource intensive, and, therefore, not scalable. We propose to apply cutting-edge natural language processing approaches to unstructured text in the electronic medical record to develop computable classifiers for variables fundamental to the study of adults with CHD. We will use two unique institutional data resources at Boston Children's Hospital and Brigham and Women's Hospital that are already populated with expert-adjudicated labels to train classifiers for key phenotypes that are poorly defined by administrative codes. These classifiers will be validated in an independent patient cohort at Vanderbilt University Medical Center and tested in new disease-specific risk prediction models. This work promises to accelerate CHD research by massively increasing the scale of the patient cohorts that can be studied and by establishing a foundation for improved evidence-based decision support for this underserved population.

项目摘要 在20世纪下半叶，要信任先天性心脏病（CHD）的手术的出现使您转移 从婴儿期致命的疾病致命的护理范式到通过 成年 具有大量心血管和其他医疗合并症的负担，并且明显增加 出现不良后果的风险，心力衰竭，心力衰竭，脑血管事故和过早死亡。 人口的出现需要新的临床护理模型作为新颖的发展 研究工具和基础设施以满足这些患者的独特特征和医疗保健需求。 成人冠心病的特征是在治疗策略中具有实质性的复杂性，依赖ERA的异质性， 终生疾病的时变意义。 组成部分的病理生理学尚不完全理解。 电子医疗记录中可用的代码既不敏感也不针对CHD CHD CHD CHD CHD诊断，也不会 充分描述许多其他显着临床特征。 数据库不太适合研究冠心病的成年人，即使目标是确定的 给定诊断的患者。 迄今为止，基于人群的研究有限的障碍将 从方法中获得统一的，大规模的多中心数据集的方法。 虽然计费代码不足，但准确地将成年人分类为冠心病所需的信息是Alledy 在电子病历记录中以临床注释的形式获得，主要由非结构化的组成（“免费”） 文本。 电子医学中的非结构化文本的尖端自然语言处理方法 开发可计算的变量分类器，用于研究成年人的冠心病 波士顿儿童医院和杨百翰的独特机构数据资源以及妇女霍斯皮塔尔 已经填充了专家审判的标签，以训练分类器的关键表型，这些型号定义较差 通过管理代码。 大学医学中心并在特定疾病的风险预测模型中进行了测试 通过大量增加患者同类群的规模来加速冠心病研究 为改善基于证据的决策决定的决策者为该人群提供了决定的基础。