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

项目总结 20世纪下半叶治疗先天性心脏病(CHD)的外科手术的出现改变了 从婴儿期致命疾病的缓解到终身慢性病的管理 成人期。目前，美国有150多万成年人患有冠心病。这些病人 有相当大的心血管和其他医疗合并症的负担，以及显著增加 不良后果的风险，如心律失常、心力衰竭、脑血管意外和过早死亡。 这一群体的出现需要新的临床护理模式以及新的 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。 成人冠心病的特点是治疗策略相当复杂，具有时代依赖性的异质性， 以及终生疾病的时变影响。这一迅速增长的人口没有得到充分的研究，而 成分性疾病的病理生理学仍不完全清楚。帐单和其他管理 电子病历中提供的代码对CHD诊断既不敏感也不特异，因此不 充分描述许多其他显著的临床特征。因此，大型管理系统中的结构化数据 数据库不太适合研究成年冠心病患者，即使目标只是确定一组 有特定诊断的患者。这构成了研究工作的主要障碍，也是主要的 迄今开展的基于人群的有限研究存在障碍。成人冠心病调查将 从建立协调、大规模、多中心数据集的方法中受益匪浅。 虽然帐单代码不够充分，但准确地对成人冠心病进行分类所需的信息已经 以临床记录的形式在电子病历中提供，主要由非结构化(“免费”)组成 文本。手动数据提取费时费力，资源密集型，因此不可伸缩。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 开发用于成人冠心病研究的基本变量的可计算分类器。我们将使用两个 波士顿儿童医院和布里格姆妇女医院独特的机构数据资源 已经填充了专家判断的标签，以训练分类器来识别定义不佳的关键表型 根据行政法规。这些分类器将在Vanderbilt的独立患者队列中得到验证 并在新的疾病特定风险预测模型中进行了测试。这项工作承诺将 通过大幅增加可研究的患者队列规模并通过以下方式加速CHD研究 为改善对这一服务不足人群的循证决策支持奠定基础。

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.