The benefits and harms of lung cancer screening in Florida

佛罗里达州肺癌筛查的好处和危害

• 批准号: 10576300
• 负责人: Jiang Bian
• 金额: $ 32.7万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10576300
• 关键词: Acceleration; Adoption; Age; American; Awareness; Cancer Control; Cancer Etiology; Cancer Patient; Cessation of life; Clinical; Colon Carcinoma; Complication; Costs and Benefits; County; Data; Data Set; Decision Making; Diagnosis; Diagnostic Procedure; Electronic Health Record; Eligibility Determination; Event; Florida; Future; General Population; Goals; Guidelines; Harm Reduction; Health Care Costs; Health Policy; Health Services Research; Healthcare; Incidence; Incidental Findings; Individual; Information Retrieval; Intervention; Knowledge; Link; Lung; Machine Learning; Malignant neoplasm of lung; Malignant neoplasm of pancreas; Malignant neoplasm of prostate; Manuals; Mathematics; Medical; Medicare; Methods; Modality; Modeling; Natural Language Processing; Outcome; Patients; Pattern; Performance; Physicians; Policies; Policy Maker; Population; Professional Organizations; Provider; Publishing; Radiology Specialty; Recommendation; Recording of previous events; Reporting; Research; Research Personnel; Resource Allocation; Resources; Risk; Smoke; Smoking; Smoking History; Specific qualifier value; Sputum Cytology Screening; Structure; Sum; Survival Rate; System; Techniques; Test Result; Thoracic Radiography; United States; United States Centers for Medicare and Medicaid Services; Use Effectiveness; Woman; Work; aged; beneficiary; cancer care; care costs; care outcomes; computable phenotypes; computed tomography screening; cost; cost effectiveness; curative treatments; data registry; deep learning model; economic evaluation; economic outcome; high risk; high risk population; innovation; low dose computed tomography; lung cancer screening; lung imaging; malignant breast neoplasm; men; models and simulation; mortality; neoplasm registry; patient oriented; phenotyping algorithm; prevent; prospective; randomized, clinical trials; repository; screening; screening guidelines; screening program; shared decision making; simulation; trend; Acceleration; Adoption; Age; American; Awareness; Cancer Control; Cancer Etiology; Cancer Patient; Cessation of life; Clinical; Colon Carcinoma; Complication; Costs and Benefits; County; Data; Data Set; Decision Making; Diagnosis; Diagnostic Procedure; Electronic Health Record; Eligibility Determination; Event; Florida; Future; General Population; Goals; Guidelines; Harm Reduction; Health Care Costs; Health Policy; Health Services Research; Healthcare; Incidence; Incidental Findings; Individual; Information Retrieval; Intervention; Knowledge; Link; Lung; Machine Learning; Malignant neoplasm of lung; Malignant neoplasm of pancreas; Malignant neoplasm of prostate; Manuals; Mathematics; Medical; Medicare; Methods; Modality; Modeling; Natural Language Processing; Outcome; Patients; Pattern; Performance; Physicians; Policies; Policy Maker; Population; Professional Organizations; Provider; Publishing; Radiology Specialty; Recommendation; Recording of previous events; Reporting; Research; Research Personnel; Resource Allocation; Resources; Risk; Smoke; Smoking; Smoking History; Specific qualifier value; Sputum Cytology Screening; Structure; Sum; Survival Rate; System; Techniques; Test Result; Thoracic Radiography; United States; United States Centers for Medicare and Medicaid Services; Use Effectiveness; Woman; Work; aged; beneficiary; cancer care; care costs; care outcomes; computable phenotypes; computed tomography screening; cost; cost effectiveness; curative treatments; data registry; deep learning model; economic evaluation; economic outcome; high risk; high risk population; innovation; low dose computed tomography; lung cancer screening; lung imaging; malignant breast neoplasm; men; models and simulation; mortality; neoplasm registry; patient oriented; phenotyping algorithm; prevent; prospective; randomized, clinical trials; repository; screening; screening guidelines; screening program; shared decision making; simulation; trend

PROJECT SUMMARY/ABSTRACT Lung cancer is the leading cause of cancer related death in both men and women in the United States. Currently, approximately 70% of lung cancer patients are diagnosed at advanced stages, and the 5-year survival rate of advanced stage lung cancer is very low, at only 16%. Investigators have been searching for effective screening modalities for the early detection of lung cancer so that patients can receive curative treatments at an early stage. When the National Lung Screening Trial (NLST) demonstrated the effectiveness of using low-dose computed tomography (LDCT) scan for lung cancer screening (LCS), researchers and physicians hope to save lives from lung cancer by screening high-risk population who aged 55 to 77 years and have a 30 pack years making history or former smokes who have quitted within the past 15 years. Since the release of the landmark NLST results, many medical associations published guidelines to recommend LDCT-based screening for individuals at high risk for lung cancer and the Centers for Medicare and Medicaid Services (CMS) also decided to cover the LCS for Medicare beneficiaries who are at high risk for lung cancer. While many efforts have been made to accelerate the dissemination the beneficial LCS, the concerns over the high false positive rates (96.4% of the positive results), invasive diagnostic procedures, postprocedural complications and health care costs may hinder the utilization of lung cancer screening. This concern was magnified as researchers and policy makers started questioning whether the complication rate and false positives in real-world settings would be even higher than the rates reported in the NLST, which was conducted in a setting with well-established facilities and proficiency in cancer care. Therefore, we propose to understand the contemporary use of lung cancer screening and associated health care outcomes and costs using data from a real-world setting. Our study has three goals: 1) to develop an innovative computable phenotype algorithm to identify high-risk and low-risk individuals for LCS from both structured and unstructured (i.e., clinical notes) electronic health record (EHR) data and to develop advanced natural language processing (NLP) methods to extract LCS related clinical information from clinical notes such as radiology reports; 2) to determine the appropriate and inappropriate use of LDCT among high-risk and low-risk individuals in Florida and to examine the test results of LDCT, the rates of invasive diagnostic procedures, postprocedural complications, and incidental findings in real-world settings; and 3) to develop and validate a microsimulation model of the clinical courses of LCS incorporating the real-world data in LCS to estimate the long-term benefits and the cost-effectiveness of LCS. Our proposed study has the potential to reduce lung cancer incidence and mortality by informing policymakers and practitioners on the appropriateness of contemporary use of LCS. This knowledge will help both patients and physicians better understand the harm- benefit tradeoff of lung cancer screening and transform such knowledge into practice to prevent avoidable postprocedural complications.

项目摘要/摘要 肺癌是美国男性和女性癌症相关死亡的主要原因。现在， 在高级阶段诊断出约70％的肺癌患者，5年生存率为 晚期肺癌非常低，仅为16％。调查人员一直在寻找有效的筛查 早期发现肺癌的方式，以便患者可以在早期接受治疗治疗 阶段。当国家肺筛查试验（NLST）证明使用低剂量的有效性 用于肺癌筛查（LCS）的计算机断层扫描（LDCT）扫描，研究人员和医生希望保存 通过筛查55至77岁的高风险人口，并拥有30年的高风险人口，从肺癌中生活 在过去15年中戒烟的历史或以前的烟。自从地标发布以来 NLST结果，许多医学协会发布了指南，建议基于LDCT的筛查 肺癌高风险的人以及医疗保险和医疗补助服务中心（CMS）也决定 覆盖有肺癌高风险的Medicare受益人的LCS。虽然许多努力 为了加速传播有益的LC，对高误报利率的担忧（96.4％） 在积极的结果中），侵入性诊断程序，术后并发症和医疗保健费用可能 阻碍了肺癌筛查的利用。作为研究人员和政策制定者，这种担忧受到了扩大 开始质疑现实环境中的并发症率和假阳性是否会更高 比在NLST中报告的费率，该价格是在设施良好的环境中进行的 精通癌症护理。因此，我们建议了解当代使用肺癌筛查 以及使用来自现实世界中的数据的相关医疗保健结果和成本。我们的研究有三个目标： 1）开发一种创新的可计算表型算法，以识别LCS的高风险和低风险个体 从结构化和非结构化（即临床注释）电子健康记录（EHR）数据中均来自 从临床上提取LCS相关的临床信息的先进自然语言处理（NLP）方法 注释，例如放射学报告； 2）确定高风险中LDCT的适当和不适当使用 佛罗里达州的低风险个体并检查LDCT的测试结果，侵入性诊断率 在现实世界中的程序，术后并发症和偶然发现； 3）开发和 验证LCS临床课程的微仿真模型，该模型纳入LCS中的现实世界数据 估计LCS的长期收益和成本效益。我们提出的研究有可能 通过向政策制定者和从业者告知适当性，降低肺癌的发病率和死亡率 当代使用LCS。这些知识将帮助患者和医生更好地了解危害 - 利益肺癌筛查的权衡并将这种知识转化为实践，以防止可避免 术后并发症。