Evaluating a Risk Prediction Model for Lung Cancer

评估肺癌风险预测模型

基本信息

批准号：
9271910
负责人：
Lori Sakoda
金额：
$ 19.27万
依托单位：
KAISER FOUNDATION RESEARCH INSTITUTE
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-05-15 至 2020-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9271910
关键词：
Active Learning Address Adult Advisory Committees Age Award Benign Biometry Body mass index California Cancer Control Cancer Detection Cancer Etiology Cancer Family Chronic Obstructive Airway Disease Cigarette Clinical Cohort Analysis Colorectal Communities Complex Custom Data Decision Aid Decision Making Diagnosis Discrimination Doctor of Philosophy Early Diagnosis Education Electronic Health Record Eligibility Determination Enrollment Environment Family history of Functional disorder Genes Genetic Genotype Goals Guidelines Health Health Surveys Image Individual Integrated Delivery of Health Care Intervention K-Series Research Career Programs Knowledge Lead Leadership Lung Lung nodule Malignant - descriptor Malignant neoplasm of lung Medical Mentors Mentorship Modeling Ovarian Performance Persons Population Positioning Attribute Practice Management Preventive service Probability Professional Organizations Prostate Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Race Radiation exposure Recording of previous events Research Research Activity Resources Risk Screening for cancer Skin Carcinoma Smoker Smoking Smoking History Smoking Status Subgroup Surveys System Time Training Training Activity Translating Unnecessary Procedures Work aged arm base biomedical informatics cancer diagnosis cancer epidemiology career clinical practice clinical predictors clinically relevant cohort cost efficient ethnic diversity experience follow-up genetic epidemiology high risk improved low-dose spiral CT lung cancer screening meetings member mortality predictive modeling programs public health relevance screening skills smoking cessation surveillance strategy translational scientist tumor whole genome

项目摘要

DESCRIPTION (provided by applicant): This Career Development Award will support Lori Sakoda, PhD, in her transition to independence as a translational researcher in lung cancer. Her long-term career goal is to inform and improve real-world strategies for lung cancer detection and control by leading transdisciplinary research that integrates analysis of complex, large-scale biomedical data. Risk prediction models could be valuably employed to optimize the benefit-to-harm ratio of screening strategies for lung cancer in smokers. To support their use in clinical practice, however, there must be convincing evidence of their predictive ability to identify smokers at highest risk for lung cancer and/or to differentiate those presenting with malignant versus benign lung nodules. Her mentored research will evaluate whether a newly developed, clinically-oriented risk prediction model for lung cancer, as proposed or modified, could aid decision-making in the context of lung cancer screening. The specific aims are to 1) validate the predictive performance of the model; 2) determine the incremental value of adding genetic and other clinical predictors to the model; and 3) examine the predictive performance of the baseline model and the best predictive extended model in persons who meet the U.S. Preventative Services Task Force eligibility criteria for lung cancer screening with low-dose computed tomography. As an exploratory aim, the predictive performance of these same two models will be assessed in the subgroup of screening- eligible persons diagnosed incidentally with lung nodules. These aims will be addressed by integrating survey, whole genome genotyping, and electronic health record (EHR) data on a large, contemporary cohort of smokers in the Kaiser Permanente Northern California (KPNC) Research Program on Genes, Environment, and Health. The proposal builds on the candidate's prior training in cancer epidemiology to fill knowledge gaps in clinical domains (lung pathophysiology, lung cancer detection and management practices, and medical decision-making) and scientific domains pertinent to integrated analysis of EHR and other complex, large-scale data (biostatistics, genetic epidemiology, and biomedical informatics), which will allow her to more effectively generate and translate scientific evidence into clinical practice. Training will be acquired from coursework, seminars, professional society meetings, and experiential learning, under the guidance of a highly qualified team of mentors and scientific advisors. She will also build clinical and scientifc partnerships essential to succeed in her current setting. The KPNC Division of Research is an ideal training environment, given its long history of important contributions to cancer screening guidelines, due to both its scientific leadership and its access to an ethnically diverse and stabl membership (currently over three million adults) for whom EHR data are kept indefinitely. The proposed plan will provide the candidate with preliminary data to develop a competitive R01 proposal, along with specialized knowledge and skills to successfully establish a transdisciplinary research program focused on optimizing strategies for lung cancer detection and control.

描述（由适用提供）：该职业发展奖将支持洛里·萨科达（Lori Sakoda，Phd），在其过渡到独立性的肺癌研究人员中。她的长期职业目标是通过领先的跨学科研究来告知和改善现实世界中的肺癌检测和控制策略，以整合复杂的大规模生物医学数据的分析。风险预测模型可以非常有价值地用于优化吸烟者肺癌筛查策略的收益与损害比率。但是，为了支持它们在临床实践中的使用，必须有令人信服的证据表明，他们的预测能力可以鉴定出肺癌最高风险的吸烟者和/或与伴有恶性肿瘤与良性肺结节的吸烟者区分开来。她的指导研究将评估新开发的，面向临床的，临床上的风险预测模型，该模型（如拟议中的或修改）是否可以在肺癌筛查的背景下有助于决策。具体目的是1）验证模型的预测性能； 2）确定向模型添加遗传和其他临床预测因子的增量值； 3）检查基线模型的预测性能以及符合美国预防服务工作队资格筛查的人的最佳预测性扩展模型，该标准使用低剂量计算机层析成像进行肺癌筛查。作为探索目的，将在偶然地用肺结诊断出的筛查人员的子组中评估这两个模型的预测性能。这些目标将通过整合调查，整个基因组基因分型和电子健康记录（EHR）数据（EHR）数据，该数据在北加州Kaiser Permanente（KPNC）基因，环境和健康研究计划中的大型现代吸烟者中。该提议建立在候选人在癌症流行病学上的先前培训的基础上，以填补临床领域的知识空白（肺病理生理学，肺癌的检测和管理实践以及医疗决策）以及与EHR和其他复杂的，大规模数据的综合分析相关的科学领域（生物统计学，遗传性疾病，遗传性增长和生物学效率），这将允许更多的效果，并允许更多地构成。进入临床实践。在高素质的导师和科学顾问团队的指导下，将从课程，半手赛，专业社会会议和经验丰富的学习中获得培训。她还将建立在当前环境中取得成功的临床和科学伙伴关系。 KPNC研究部是一个理想的培训环境，鉴于其对癌症筛查指南的重要贡献的悠久历史，由于其科学领导层及其获得了富有的养殖潜水员和Stabl会员资格（目前超过300万成人），因此EHR数据无限期地保存。拟议的计划将为候选人提供初步数据，以开发有竞争力的R01提案，并提供专门的知识和技能，以成功建立跨学科研究计划，旨在优化用于肺癌检测和控制策略的策略。