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 20世纪下半叶治疗先天性心脏病（CHD）的手术冒险使 从婴儿期致命的疾病致命的护理范式到通过 成年。现在在美国有超过150万CHD的成年人。这些患者 心血管和其他医疗合并症大幅燃烧，并且明显增加 出现心律不齐，心力衰竭，脑血管事故和过早死亡等不良结果的风险。 该人群的出现需要新的临床护理模型以及新颖的发展 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， 以及终身疾病的时变意义。理解了这种水伤人群， 成分疾病的病理生理学仍然不完全理解。计费和其他行政 电子病历中可用的代码要么敏感或针对CHD诊断，因此不要 适当描述许多其他显着临床特征。结果，大型管理中的结构化数据 数据库并不适合研究冠心病的成年人，即使目标只是确定一组 给定诊断的患者。这构成了研究工作的主要障碍，是主要的 迄今为止，基于人群的研究有限的障碍。成人CHD调查将 从方法中获得了极大的益处，以建立统一的大规模，多中心数据集。 虽然计费代码不足，但将成人准确分类的信息已经 在电子病历中以临床注释的形式获得，主要由非结构化（“免费”）组成 文本。手动数据提取是实验室，资源密集的，因此不可扩展。我们建议申请 电子病历中非结构化文本的尖端自然语言处理方法 开发用于变量的可计算分类器，该变量是针对成年冠心病研究的基础的。我们将使用两个 波士顿儿童医院，杨百翰和妇女医院的独特机构数据资源 已经填充了专家判决的标签，以训练分类器的关键表型，这些表型的定义不佳 通过管理代码。这些分类器将在范德比尔特的独立患者队列中进行验证 大学医学中心并在新的疾病特异性风险预测模型中进行了测试。这项工作有望 通过大量增加可以研究的患者队列的规模，并通过 为改善服务不足的人群改善基于证据的决策支持的基础。