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导致1550万癌症幸存者 目前在美国，幸存者人数预计到2026年将达到2000万。癌症和 它的治疗通常会导致长期的影响，包括持续的症状和功能缺陷 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，可以在其中检查 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)和罕见病办公室指出，患有 癌症等慢性疾病会经历一系列症状群，对 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 研究为有效的症状管理建立证据基础。我们的建议是对一项 NINR/NCI从这次研讨会中产生的联合计划声明。大多数先前关于症状的研究 癌症中的聚集性集中于接受癌症急性期治疗的特定常见疾病的患者。 癌症通常在学术转介中心接受治疗。我们的研究将通过关注早期的 阶段，社会人口统计学上不同的成年癌症幸存者在普通美国肿瘤学实践中接受治疗。 我们将使用之前调查的5,506名成人癌症队列的数据来研究症状群 幸存者在21-84岁之间被初步诊断为患有7种不同癌症类型中的1种，这些人是在 与4个基于人群的监测、流行病学和最终结果(SEER)癌症登记机构建立合作伙伴关系 州(加州、新泽西州、洛杉矶)。这一队列的首次调查是在首次癌症诊断后9个月的中位数进行的， 6个月后进行随访调查。SEER注册数据包括详细临床和治疗数据， 调查收集了常见和高度常见的8个症状和功能状态领域的信息 对癌症幸存者的影响，以及许多其他社会人口和经济变量。在目标1中，我们将 使用创新的心理测量法和统计学方法确定症状群和亚组概况的流行率 方法：研究方法。我们将评估社会人口学(例如，年龄、性别、种族、收入、教育)和临床 与每个症状群相关的特征(例如，癌症类型、分期、治疗和合并症)以及 幸存者亚群。在目标2中，我们将使用最先进的技术来研究症状群的时间模式 潜伏期分析。在这两个目标中，我们将评估几个可变因素在患者和 医疗保健系统水平，为未来的干预研究提供信息。在目标3中，我们将调查 循证症状和共病护理与症状群的患病率和发展轨迹 65岁及以上幸存者子组的相关医疗保险索赔数据。我们的结果将使我们能够 对症状丛集现象的理解，将提供新的知识基础，将 帮助为未来的干预措施提供信息，以减轻症状群的不利影响。

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.