Identifying Symptom Clusters in People with Cancer

描述（由申请人提供）： 症状和副作用，如疲劳；脱发、口腔疼痛、疼痛和睡眠问题是癌症治疗期间和治疗后的重要问题。大多数关于症状的研究都嵌入在单一症状框架中，但癌症治疗的实际经验是症状以群体或集群的形式出现。用于指导研究的范式与癌症患者的“现实世界”经验之间的差距是建立癌症护理症状管理知识库的主要限制。该项目的目的是通过识别癌症治疗期间经历的症状群、确定特定的人口统计和临床变量是否可以预测症状群成员资格、以及随着时间的推移检查症状群成员资格与情绪和身体功能的关系来解决这一差距。我们将使用之前四项研究中收集的数据，其中包括有关症状、情绪和功能的信息，但并非全部使用相同的方法来获取这些信息。这些研究有所不同这一事实是该项目的优势，因为我们将对每项研究分别进行统计分析，并且能够根据治疗类型（放射治疗或化疗）查看聚类，看看它们是否看起来相似，以确认或交叉验证研究结果。我们将使用年龄、疾病阶段和合并症等变量来探索症状群成员的预测因素。这些信息很有用，因为它最终可能会告诉我们谁最有可能出现多种严重症状。分层聚类分析将用于识别和交叉验证每种类型的癌症治疗和癌症诊断（乳腺癌或肺癌）中的症状群。建立集群成员资格后，我们将使用层次线性模型来检查集群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经研究的问题，并将为转向多种症状模型的其他研究人员提供必要的信息。该项目的结果将提供有关一种方法的重要信息，该方法被认为可用于开展症状群工作，但除了开发评估工具外，从未应用于已发表的癌症症状工作。因为我们正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，所以分析结果将提供有关比较两种不同治疗方法时症状群的维持程度的信息，并可能挑战我们传统的临床假设，即人们经历的症状群是治疗类型的结果。

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.