Identifying Recurrent Non-Hodgkin Lymphoma in Electronic Health Data

项目摘要/摘要 电子健康记录(EHR)为有效研究患者的预后提供了丰富的资源 诊断为非霍奇金淋巴瘤(NHL)。然而，癌症复发不需要报告给 癌症登记处，因此，需要基于EHR数据的创新算法来有效地识别这一点 以人群为基础的研究的重要患者结局。该建议旨在首先使用 基于规则的方法，由专家知识提供信息；第二，使用数据驱动的机器学习 检测两种常见组织学亚型NHL复发的方法：侵袭性弥漫性大B细胞 淋巴瘤(DLBCL)和更无痛性滤泡性淋巴瘤(FL)。约20-25%的DLBCL和25%- 35%的FL幸存者将经历疾病复发，但复发的可改变危险因素在很大程度上 未知。将使用纵向收集的来自两个大公司的EHR数据开发特定亚型的算法 服务于不同人口的医疗系统，并有共同的开展历史 协作性癌症研究。这项工作的长期目标是将经过验证的算法应用于其他 具有共享数据基础设施的医疗保健系统，以建立对诊断为 NHL，以确定淋巴瘤预后的决定因素，并推动这一未被充分研究的研究领域。 拟议的项目将包括1,128例DLBCL和519例确诊时18岁及以上的FL病例(2000- 2018)，从亨利·福特医疗系统(密歇根州底特律)和迈耶斯初级保健中心跟进到2021年 研究所/瑞安医疗集团(马萨诸塞州伍斯特)。基本的诊断后数据，包括详细的治疗 将汇编所有研究参与者的病史、肿瘤特征和医疗保健利用情况，以及 基于文本的临床笔记和报告。拟议的研究旨在：1)开发和评估基于规则的 集成来自健康声明、EHR和肿瘤登记的数据的算法，包括特定的治疗数据 和相关程序的结果；2)采用与自然语言相结合的机器学习方法 语言处理，以提高算法性能。我们将验证针对每个NHL亚型的算法 对照HFHS的黄金标准复发登记和两个研究地点的有针对性的EHR审查。积极的一面 计算每种算法的预测值。 通过在现实世界的电子健康数据中成功识别出复发的NHL患者，我们将成为第一个 逐步确定增加患者复发风险的因素。这群非霍奇金淋巴瘤患者将 受益于长达21年的临床随访和标准化收集的详细治疗数据 电子数据资源。通过EHR数据准确捕获NHL患者的疾病复发将 促进将这些算法应用于具有共享数据基础设施的附加医疗保健系统， 并允许高效地对关键但未得到充分研究的患者进行大规模、基于人群的研究 结果。

PROJECT SUMMARY/ABSTRACT Electronic health records (EHRs) represent a rich resource for the efficient study of outcomes for patients diagnosed with non-Hodgkin lymphoma (NHL). However, cancer recurrence is not required to be reported to cancer registries, and as a result, innovative algorithms based in EHR data are needed to validly identify this important patient outcome for population-based studies. This proposal aims to construct algorithms first using a rule-based approach informed by expert knowledge, and second using a data-driven machine learning approach to detect recurrence of two common histologic subtypes of NHL: the aggressive diffuse large B-cell lymphoma (DLBCL) and the more indolent follicular lymphoma (FL). Approximately 20-25% of DLBCL and 25- 35% of FL survivors will experience disease recurrence, yet modifiable risk factors for recurrence are largely unknown. Subtype-specific algorithms will be developed using longitudinally collected EHR data from two large healthcare systems serving demographically diverse populations, and who share a history of conducting collaborative cancer research. The long-term goal for this work is to apply the validated algorithms to additional healthcare systems with shared data infrastructure to establish a multi-site study of patients diagnosed with NHL to identify determinants of lymphoma outcomes and advance this understudied field of research. The proposed project will include 1,128 DLBCL and 519 FL cases aged 18 years and older at diagnosis (2000- 2018) with follow-up through 2021 from Henry Ford Health System (Detroit, MI) and the Meyers Primary Care Institute/Reliant Medical Group (Worcester, MA). Essential post-diagnosis data including detailed treatment history, tumor characteristics, and healthcare utilization will be compiled for all study participants, along with text-based clinical notes and reports. The proposed research aims to: 1) develop and evaluate rule-based algorithms integrating data from health claims, EHRs, and tumor registries, including specific treatment data and results from relevant procedures; and 2) adopt a machine learning approach integrated with natural language processing to improve algorithm performance. We will validate the algorithms for each NHL subtype against a gold-standard recurrence registry at HFHS and targeted EHR review at both study sites. The positive predictive value of each algorithm will be calculated. By successfully identifying patients with recurrent NHL in real-world electronic health data, we will take the first step towards identifying factors that increase a patient’s risk of recurrence. This cohort of patients with NHL will benefit from up to 21 years of clinical follow-up and detailed treatment data collected from standardized electronic data resources. Accurately capturing disease recurrence in patients with NHL through EHR data will facilitate the application of these algorithms to additional healthcare systems with shared data infrastructure, and allow for the efficient conduct of large-scale, population-based studies of critical, yet understudied, patient outcomes.