Large scale clinical and economic impact analysis of potentially malignant incidental findings in radiology reports

放射学报告中潜在恶性偶然发现的大规模临床和经济影响分析

• 批准号: 10363655
• 负责人: Martin Gunn
• 金额: $ 63.64万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-03 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10363655
• 关键词: Abdomen; Active Learning; Address; Adherence; Adrenal Glands; Age; Agreement; Angiography; Anxiety; Caregivers; Categories; Cessation of life; Chest; Clinical; Clinical Data; Clinical Practice Guideline; Code; Communication; Communities; Data; Data Set; Databases; Development; Diagnosis; Disease; Documentation; Economics; Ensure; Epidemic; Expenditure; Funding; Future; Gold; Growth; Guidelines; Health; Healthcare; Hospitals; Image; Imaging technology; Incidental Findings; Institutes; Interdisciplinary Study; Investigation; Kidney; Lead; Link; Liver; Lung; Lung nodule; Machine Learning; Magnetic Resonance Imaging; Malignant - descriptor; Malignant Neoplasms; Medical Informatics; Medical center; Methods; Modeling; Modernization; Natural Language Processing; Oncology; Organ; Outcome; Outcomes Research; Pancreas; Pancreatic Cyst; Patient Noncompliance; Patient risk; Patients; Performance; Prevalence; Pulmonary Embolism; Radiation exposure; Radiology Specialty; Recommendation; Reporting; Research; Research Project Grants; Risk; Risk Factors; Running; Scanning; Semantics; Services; Technology; Testing; Text; Thyroid Gland; Thyroid Nodule; Training; Universities; Washington; base; cancer care; cancer diagnosis; cancer risk; clinical database; cohort; comorbidity; cost; data repository; economic evaluation; economic impact; follow-up; health care delivery; health care service organization; health economics; improved; machine learning method; mortality; novel; open source; overtreatment; patient population; radiological imaging; radiologist; repository; structured data; surveillance imaging; systematic review; tumor

Abstract Unexpected findings, or incidentalomas, are increasing dramatically with the growth in the use of imaging technology within healthcare organizations. Incidentalomas may indicate significant health problems, such as malignancy in the medium or long term. However, they also may lead to overinvestigation, unnecessary radiation exposure, overtreatment, substantial downstream expenditures, and patient anxiety. Several systematic reviews have explored the prevalence and outcomes of incidentalomas. These studies used inconsistent and often inappropriate synthesis methods, commonly only focusing on one imaging scan or organ in a very limited number of patients. As a result, there is need for large-scale study of incidentalomas that can inform their follow up and guide efforts to optimize health outcomes. To address this need, we propose to build natural language processing (NLP) approaches to identify cancer-related incidentalomas reported in radiology reports (Aim 1) and to create the first large-scale incidentaloma database covering over half-a-million patients (Aim 2). Our research dataset will contain radiology reports, clinical notes containing imaging orders, as well as structured data such as demographic information (e.g., age) and diagnoses codes of patients who received radiologic imaging tests in University of Washington Medical Center (UWMC), Harborview Medical Center (HMC), Seattle Cancer Care Alliance (SCCA), and Northwest Hospital and Medical Center (NWMC) between 2007-2019. Our patient population will be linked to Hutchinson Institute for Cancer Outcomes Research (HICOR) data repository for detailed cancer outcomes and claims data. The created database will be used for clinical and economic analysis of incidentalomas (Aim 3). We will (1) evaluate the concordance between radiologists' documentation of incidentaloma follow-up and established clinical guidelines for thyroid, lung, adrenal, kidney, liver, and pancreas incidentalomas, (2) determine risk of subsequent cancer diagnosis and median survival for each category of incidentaloma, and (3) determine the incremental cost associated with follow-up imaging in patients with incidentalomas. All models and their implementations produced during the execution of this project will be shared with the community as open source. Additionally, the de-identified incidentaloma database will be made available to the research community under a data use agreement. By identifying risk factors for cancer diagnosis and death for common incidental findings, we will be able to provide critical information for future clinical practice guideline development and appropriate use criteria. We assembled a highly interdisciplinary team of experts in NLP, medical informatics, radiology, oncology, health outcomes, and health economics to ensure the successful completion of the proposed project.

抽象的 意外的发现或偶然发现，随着成像的使用增长而显着增加 医疗保健组织中的技术。附词瘤可能表明重大健康问题，例如 中等或长期的恶性肿瘤。但是，它们也可能导致过度投资，不必要 辐射暴露，过度治疗，大量下游支出和患者焦虑。一些 系统评价探讨了偶然瘤的患病率和结果。这些研究使用了 不一致且通常不适当的合成方法，通常仅专注于一个成像扫描或 器官在非常有限的患者中。结果，需要进行大规模研究 这可以告知他们的后续行动，并指导优化健康结果的努力。为了满足这一需求，我们 建议建立自然语言处理（NLP）方法以识别与癌症相关的杂物 在放射学报告（AIM 1）中报告，并创建第一个涵盖的大规模杂瘤数据库 数百万患者（AIM 2）。我们的研究数据集将包含放射学报告，临床注释包含 成像订单以及结构化数据，例如人口统计信息（例如年龄）和诊断代码 在华盛顿大学医学中心（UWMC）接受放射成像测试的患者中， Harbourview Medical Center（HMC），西雅图癌症护理联盟（SCCA）以及西北医院和医疗 2007 - 2019年之间的中心（NWMC）。我们的患者群体将与哈钦森癌症研究所有关 结果研究（HICOR）数据存储库，用于详细的癌症结果和索赔数据。创建 数据库将用于偶然瘤的临床和经济分析（AIM 3）。我们将（1）评估 放射科医生的偶然瘤随访文档与已建立的临床之间的一致性 甲状腺，肺，肾上腺，肾脏，肝脏和胰腺偶然指南，（2）确定有风险 随后的癌症诊断和中位疾病中的中位生存期，（3）确定 与附带瘤患者的随访成像相关的增量成本。所有模型及其 在执行该项目期间生产的实施将与社区共享，以开放 来源。此外，将为研究提供去识别的杂物数据库 根据数据使用协议的社区。通过确定癌症诊断和常见死亡的危险因素 偶然发现，我们将能够为未来的临床实践指南提供关键信息 开发和适当的使用标准。我们组建了一个高度跨学科的NLP专家团队 医学信息学，放射学，肿瘤学，健康成果和健康经济学，以确保成功 拟议项目的完成。