Identifying Symptom Clusters in People with Cancer

识别癌症患者的症状群

• 批准号: 6898724
• 负责人: LILLIAN M. NAIL
• 金额: $ 21.3万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-06-01 至 2007-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6898724
• 关键词: breast neoplasm /cancer diagnosis; clinical research; functional ability; human subject; human therapy evaluation; longitudinal human study; lung neoplasms; mood disorders; neoplasm /cancer chemotherapy; neoplasm /cancer radiation therapy; patient oriented research; sign /symptom; statistics /biometry; therapy adverse effect

DESCRIPTION (provided by applicant): Symptoms and side effects like fatigue; hair loss, sore mouth, pain, and problems sleeping are important issues during and following cancer treatment. Most research on symptoms is embedded in a single symptom framework but the reality of the experience of cancer treatment is that symptoms occur in groups or dusters. This gap between the paradigm used to guide research and the "real world" experience of people with cancer is a major limitation in building the knowledge base for symptom management in cancer care. The purpose of this project is to address this gap by identifying symptom clusters experienced during cancer treatment, determining if specific demographic and clinic variables predict symptom cluster membership, and examining the relationship of symptom cluster membership to mood and physical function over time. We will use data collected in four prior studies that included information about symptoms, mood, and function but did not all use the same approach for getting this information. The fact that the studies are somewhat different is a strength for this project because we will be doing the statistical analysis separately for each study and will be able to look at the clusters to see if they look similar based upon type of treatment (radiation treatment or chemotherapy) to confirm or cross-validate the findings. We will explore predictors of symptom cluster membership using variables such as age, stage of disease, and comorbidity. This information is helpful because it may eventually tell us who is at highest risk for experiencing numerous severe symptoms. Hierarchical cluster analysis will be used to identify and cross-validate the symptom clusters in each type of cancer treatment and by cancer diagnosis (breast or lung cancer). After cluster membership is established, we will use hierarchical linear modeling to examine the relationship of cluster membership to mood and function. This project is innovative because it addresses an unstudied problem and will provide information essential to other investigators who are moving to multiple symptom models. The results of this project will provide important information about an approach that is assumed to be useful in pursuing work on symptom clusters but has never been applied in published work on cancer symptoms except in developing assessment tools. Because we are doing our analyses within two types of cancer treatment and cross-validating within each type of treatment for breast cancer and across two types of treatment for lung cancer, the results of the analyses will provide information about how well the symptom clusters hold up when the two different treatments are compared and may challenge our traditional clinical assumption that the clusters of symptoms people experience are a result of type of treatment.

描述(申请人提供)：症状和副作用，如疲劳、脱发、口腔酸痛、疼痛和睡眠问题是癌症治疗期间和之后的重要问题。大多数关于症状的研究都被嵌入到一个单一的症状框架中，但癌症治疗经验的现实是，症状是成群结队地出现的。用于指导研究的范例与癌症患者的“真实世界”体验之间的这种差距是建立癌症护理中症状管理的知识库的主要限制。该项目的目的是通过确定癌症治疗过程中经历的症状群，确定特定的人口统计学和临床变量是否预测症状群成员资格，并检查症状群成员资格与情绪和身体功能随时间的关系，来解决这一差距。我们将使用在之前的四项研究中收集的数据，这些数据包括有关症状、情绪和功能的信息，但并不都使用相同的方法来获得这些信息。这些研究略有不同这一事实是该项目的一个优势，因为我们将分别对每项研究进行统计分析，并将能够查看分组，以确定它们是否基于治疗类型(放射治疗或化疗)看起来相似，以确认或交叉验证结果。我们将使用诸如年龄、疾病阶段和共病等变量来探索症状群成员的预测因素。这一信息是有帮助的，因为它最终可能会告诉我们，谁是经历许多严重症状的最高风险。分级聚类分析将用于识别和交叉验证每种癌症治疗和癌症诊断(乳腺癌或肺癌)中的症状群。在建立聚类成员资格后，我们将使用分层线性建模来检查聚类成员资格与情绪和功能的关系。该项目具有创新性，因为它解决了一个未研究的问题，并将为其他正在转向多种症状模型的研究人员提供必要的信息。该项目的结果将提供关于一种方法的重要信息，这种方法被认为对开展症状群的工作很有用，但除了开发评估工具外，从未被用于已发表的关于癌症症状的工作。由于我们是在两种类型的癌症治疗中进行分析，并在每种类型的乳腺癌治疗和两种类型的肺癌治疗中进行交叉验证，因此分析的结果将提供有关两种不同治疗方法进行比较时症状群的表现如何的信息，并可能挑战我们的传统临床假设，即人们经历的症状群是治疗类型的结果。