Beyond Body Mass Index: Bringing Body Composition into Oncology Practice

超越体重指数：将身体成分纳入肿瘤学实践

• 批准号: 10378110
• 负责人: Elizabeth Marjorie Cespedes Feliciano
• 金额: $ 17.94万
• 依托单位: KAISER FOUNDATION RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10378110
• 关键词: Active Learning; Address; Adipose tissue; Aftercare; Assessment tool; Biometry; Body Composition; Body Weight decreased; Body mass index; Breast Cancer Patient; California; Cancer Biology; Cancer Patient; Caring; Chronic Disease; Clinic; Clinical; Clinical Data; Colorectal; Colorectal Cancer; Colorectal Surgery; Competence; Computer software; Computerized Medical Record; Data; Data Set; Ensure; Epidemiology; Exercise; Fatty acid glycerol esters; Feedback; Future; Goals; Gold; Grant; Guidelines; Health Personnel; Image; Image Analysis; Institution; Integrated Health Care Systems; Intervention; K-Series Research Career Programs; Knowledge; Laboratories; Malignant Neoplasms; Manuals; Masks; Measures; Mediator of activation protein; Medical; Medical Imaging; Mentors; Methodology; Methods; Modeling; Muscle; Muscular Atrophy; Nature; Nonmetastatic; Normal Range; Obesity; Older Population; Oncologist; Oncology; Outcome; Patients; Performance; Persons; Pharmacy facility; Phenotype; Proteins; Proxy; Public Health; Quality of life; Research; Research Personnel; Research Training; Resources; Risk; Role; Scanning; Sensitivity and Specificity; Skeletal Muscle; Surgeon; Surgical complication; Testing; Time; Training; Translational Research; Treatment-related toxicity; Weight; Writing; X-Ray Computed Tomography; advanced analytics; analytical method; base; biomedical imaging; cancer care; cancer epidemiology; cancer survival; cancer type; clinical decision support; clinical decision-making; clinical practice; clinical translation; clinically relevant; cohort; comorbidity; cost effective; data visualization; demographics; electronic data; ethnic diversity; experience; healthy weight; high body mass index; improved; lifestyle intervention; malignant breast neoplasm; member; mortality; muscle form; nutrition; patient population; personalized cancer care; precision oncology; predictive modeling; prognostic; prognostication; programs; prototype; responsible research conduct; risk prediction; risk prediction model; sarcopenia; sarcopenic obesity; skills; software development; standard of care; strength training; study population; support tools; survivorship; symposium; tool; translational scientist; tumor

PROJECT SUMMARY/ABSTRACT This Career Development Award will support Elizabeth Feliciano, ScD, in her transition to independence as a translational researcher utilizing electronic data to improve cancer care. Her long-term goals are to elucidate how body composition influences cancer outcomes; develop electronic tools that use routinely collected data to improve prognostication and guide clinical interventions; and collaborate to use these electronic tools in interventions that improve cancer outcomes. Sarcopenia (low skeletal muscle mass) and sarcopenic obesity (sarcopenia with excess adiposity) are common in cancer patients, typically occult, and associated with surgical complications, treatment toxicity, poor quality of life, and reduced survival across a variety of cancer types. Despite being strongly prognostic and modifiable, body composition data is under-utilized in clinical decision-making. To address this, Dr. Feliciano will develop cost-effective, clinic-friendly methods to identify patients with sarcopenia. Next, she will develop a tool that communicates body composition information to a cancer care team. Using rich electronic medical record and imaging data from a cohort of nearly 6,000 non-metastatic colorectal and breast cancer patients, she will: validate software to automatically assess body composition from clinically-acquired imaging (Aim 1); develop a predictive model to identify sarcopenia based on electronic medical data, for use in cancer patients who do not undergo imaging (Aim 2); and integrate body composition information into established risk prediction models for colorectal cancer used in clinical practice and present this information in an understandable and actionable format to clinicians (Aim 3). Dr. Feliciano is well suited to perform this research based on her experience in body composition assessment and cancer epidemiology, an exceptional mentoring team who will ensure scientific rigor and clinical relevance, and the unique setting within an integrated healthcare system with a comprehensive electronic medical record and an ethnically diverse patient population of 4.1-million members. Her institution, Kaiser Permanente Northern California, provides an ideal training ground for her to gain needed skills through coursework, conferences, seminars, and experiential learning in the following domains: advanced analytic methods (predictive modeling, biomedical image analysis, and data visualization); cancer biology and clinical cancer care; and core competencies of an independent investigator (grant-writing, responsible conduct of research, and clinical translation of research).Through the proposed research and training, she will acquire the preliminary data, knowledge, and clinical and scientific partnerships she needs to develop a competitive R01 proposal and to establish a successful research program focused on optimizing use of electronic data to improve cancer care.

项目摘要/摘要 该职业发展奖将支持伊丽莎白·费利西亚诺，SCD，向独立过渡，成为 一位利用电子数据来改善癌症护理的翻译研究人员。她的长期目标是澄清 身体成分如何影响癌症结果；开发电子工具，使用常规收集的数据来 改善预测和指导临床干预；并协作将这些电子工具用于 改善癌症预后的干预措施。 骨质疏松症(骨骼肌低质量)和骨质疏松症(骨质疏松症伴过度肥胖)是 常见于癌症患者，一般隐匿，并伴有手术并发症、治疗毒性、不良反应。 提高生活质量，降低各种癌症类型的存活率。尽管它具有很强的预见性 可修改的身体成分数据在临床决策中未得到充分利用。 为了解决这个问题，费利西亚诺博士将开发经济高效、临床友好的方法来识别患有 石棺减少症。接下来，她将开发一种工具，将身体成分信息传达给癌症护理人员 一队。使用来自近6,000名非转移性肿瘤患者的丰富电子病历和影像数据 结直肠癌和乳腺癌患者，她将：验证自动评估身体成分的软件 从临床获得的成像(目标1)；开发一种基于电子技术的预测模型来识别石棺减少症 医学数据，用于未接受成像的癌症患者(目标2)；以及整合体成分 已建立的结直肠癌风险预测模型在临床实践中的应用和现状 以临床医生可理解和可操作的形式提供这些信息(目标3)。 根据她在身体成分方面的经验，费利西亚诺博士非常适合进行这项研究。 评估和癌症流行病学，一个杰出的指导团队，将确保科学严谨和 临床相关性，以及在集成医疗系统中的独特设置，具有全面的 电子病历和410万名不同种族的患者人口。她的机构， Kaiser Permanente North California，为她提供了一个理想的培训场所，通过 以下领域的课程作业、会议、研讨会和体验式学习：高级分析 方法(预测建模、生物医学图像分析和数据可视化)；癌症生物学和临床 癌症护理；以及独立调查员的核心能力(赠款的编写、负责任的行为 研究和临床研究翻译)。通过建议的研究和培训，她将获得 她开发具有竞争力的R01所需的初步数据、知识以及临床和科学合作伙伴关系 提议并建立一个成功的研究方案，重点是优化电子数据的使用，以 改善癌症护理。