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·费利卡诺（Elizabeth Feliciano，SCD 翻译研究人员利用电子数据改善癌症护理。她的长期目标是阐明 身体成分如何影响癌症的结局；开发电子工具，这些工具通常使用常规收集的数据 改善预后和指导临床干预措施；并合作使用这些电子工具 改善癌症结局的干预措施。 肌肉减少症（骨骼肌质量低）和肌肉减少性肥胖症（脂肪过多的肌肉减少症） 在癌症患者中常见，通常是神秘的，并且与手术并发症，治疗毒性相关，较差 生活质量以及各种癌症类型的生存降低。尽管预后很强和 在临床决策中，可修改的身体组成数据不足。 为了解决这个问题，Feliciano博士将开发具有成本效益的诊所友好方法，以识别患者 肌肉减少症。接下来，她将开发一种将身体组成信息传达给癌症护理的工具 团队。使用丰富的电子病历和来自近6,000个非转移性的队列的成像数据 结直肠癌和乳腺癌患者，她将：验证软件自动评估身体成分 从临床获得的成像（AIM 1）；开发一个预测模型，以鉴定基于电子的肌肉减少症 医学数据，用于不接受影像成像的癌症患者（AIM 2）；并整合身体成分 在临床实践中使用的结直肠癌的既定风险预测模型的信息 这些信息以临床医生的可理解和可行的格式（AIM 3）。 Feliciano博士非常适合根据她在身体成分方面的经验进行这项研究 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，以及综合医疗系统中的独特环境，具有全面的 电子病历和410万成员的种族多样化的患者人群。她的机构 Kaiser Permanente北加州，为她提供了理想的培训理由，以通过 课程工作，会议，研讨会和经验学习在以下领域：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；独立调查员的核心能力（赠款，负责任的行为 研究和研究的临床翻译）。通过拟议的研究和培训，她将获得 她需要开发有竞争力的R01的初步数据，知识以及临床和科学伙伴关系 提案并建立成功的研究计划，重点是优化电子数据的使用 改善癌症护理。