Elucidating the Prevalence, Etiology, and Trajectories of Symptom Clusters in Early Cancer Survivors

基础科学发现和癌症治疗创新使1550万癌症幸存者 目前在美国，幸存者人数估计到2026年将达到2000万。癌症和 其治疗通常会引起长期影响，包括持续症状和功能缺陷， 对幸存者的生活质量产生负面影响。癌症幸存者是一个理想的人群，在其中检查多个 共同出现的症状，称为“症状群”。2017年，症状科学专家小组由 国家护理研究所（NINR）和罕见疾病办公室指出， 诸如癌症的慢性疾病经历一系列症状群， 健康相关生活质量（HRQOL）。该小组呼吁通过症状群来推进症状科学 研究建立有效症状管理的证据基础。我们的建议是为了回应一个 NINR/NCI联合计划公告，来自这次研讨会。大多数关于症状的先前研究 癌症的集群集中在经历癌症治疗急性期的患者， 通常在学术转诊中心治疗的癌症。我们的研究将通过关注早期 在美国一般肿瘤学实践中接受治疗的不同分期、社会人口统计学上不同的成年癌症幸存者。 我们将使用先前调查的5，506名成人癌症患者的数据来研究症状群 最初被诊断为21-84岁之间的幸存者，患有7种不同癌症类型中的1种， 与3个国家的4个基于人群的监测、流行病学和结局（SEER）癌症登记处建立伙伴关系 州（CA，NJ，LA）。该队列在首次癌症诊断后9个月的中位数时首次接受调查， 6个月后进行随访调查。SEER登记数据包括详细的临床和治疗数据， 调查收集了8个领域的症状和功能状态的信息， 对癌症幸存者以及许多其他社会人口和经济变量有影响。在目标1中，我们 使用创新的心理测量和统计方法确定症状群和亚组概况的患病率 方法.我们将评估社会人口统计学（例如，年龄、性别、种族-民族、收入、教育）和临床 特性（例如，癌症类型、分期、治疗和合并症）， 幸存者亚组。在目标2中，我们将使用最先进的方法研究症状群的时间模式。 潜在变迁分析在这两个目标中，我们将评估几个可变因素在患者中的作用， 卫生保健系统水平，为未来的干预性研究提供信息。在目标3中，我们将研究 循证症状和合并症护理与症状群的患病率和轨迹， 与65岁及以上幸存者亚组的医疗保险索赔数据相关联。我们的研究结果将使一个更富有的 了解症状群的现象，并将提供一个新的知识基础， 有助于为未来的干预措施提供信息，以减轻症状群的不良影响。

Basic science discoveries and cancer treatment innovations have led to 15.5 million cancer survivors currently in the United States, and the number of survivors is estimated to reach 20 million by 2026. Cancer and its treatment often cause long-term effects, including both persistent symptoms and functional deficits that negatively impact survivors’ quality of life. Cancer survivors are an ideal population in which to examine multiple co-occurring symptoms, termed “symptom clusters.” A 2017 panel of experts in symptom science convened by the National Institute of Nursing Research (NINR) and the Office of Rare Diseases noted that patients with chronic conditions such as cancer experience an array of symptom clusters that have a negative impact on health-related quality of life (HRQOL). This panel called for advancing symptom science through symptom cluster research to build the evidence base for effective symptom management. Our proposal is in response to an NINR/NCI joint program announcement that emerged from this workshop. Most prior research on symptom clusters in cancer has focused on patients undergoing the acute phase of cancer treatment for specific common cancers often treated in academic referral centers. Our study will address these limitations by focusing on early stage, sociodemographically diverse adult cancer survivors treated in general US oncology practices. We will investigate symptom clusters using data from a previously surveyed cohort of 5,506 adult cancer survivors initially diagnosed between 21-84 years of age with 1 of 7 distinct cancer types who were recruited in partnership with 4 population-based Surveillance, Epidemiology, and Ends Results (SEER) cancer registries in 3 states (CA, NJ, LA). This cohort was first surveyed at a median of 9 months after initial cancer diagnosis with a follow-up survey conducted 6 months later. The SEER registry data include detailed clinical and treatment data, and the survey collected information on 8 domains of symptoms and functional status common and highly impactful in cancer survivors, and numerous other sociodemographic and economic variables. In Aim 1, we will identify the prevalence of symptom clusters and subgroup profiles using innovative psychometric and statistical methods. We will evaluate sociodemographic (e.g., age, sex, race-ethnicity, income, education) and clinical characteristics (e.g., cancer type, stage, treatment, and comorbidity) associated with each symptom cluster and survivor subgroup. In Aim 2 we will investigate the temporal patterns of symptom clusters using state-of-the-art latent transition analysis. In both aims, we will evaluate the role of several mutable factors at the patient and healthcare system levels to inform future interventional research. In Aim 3 we will investigate the association of evidence-based symptom and comorbidity care with the prevalence and trajectory of symptom clusters using linked Medicare claims data for the subgroup of survivors ages 65 and over. Our results will enable a richer understanding of the phenomenon of symptom clusters and will provide a new foundation of knowledge that will help inform future interventions to mitigate the adverse effects of symptom clusters.