Ontario Cancer Institute and Faculty of Nursing, University of Toronto, Toronto, Ontario, Canada.
Eur J Cancer. 2012 Nov;48(16):3073-81. doi: 10.1016/j.ejca.2012.04.008. Epub 2012 May 26.
Cluster identification has emerged as a priority for symptom research. Variation in statistical approaches has hampered the identification of common clusters that should be targeted for intervention. The purpose of this study was to identify and validate common symptom clusters in a large population-based cohort of ambulatory cancer subjects.
This descriptive, factor analysis study used bootstrap methods to derive a stable factor structure to identify symptom clusters in a population-based sample of cancer patients. Subjects were identified from a provincial symptom database and linked to other provincial databases. Symptom clusters were validated using confirmatory factor analysis in a randomly selected portion of the sample and model fit examined using common goodness of fit criteria.
The cluster cohort included 14,247 subjects. Three symptom clusters were identified: fatigue-sickness symptoms (tiredness, nausea, drowsiness and shortness of breath), emotional distress (depression and anxiety), and a poor sense of well-being (appetite and well-being). These clusters were stable across most sub-populations in the cohort.
The identification of common symptom clusters using robust statistical methods will help to yield targets to improve symptom management and identify populations at risk for worse disease outcomes.
聚类识别已成为症状研究的重点。统计方法的差异阻碍了常见聚类的识别，这些聚类应该成为干预的目标。本研究的目的是在一个大型基于人群的门诊癌症患者队列中识别和验证常见的症状聚类。
本描述性、因子分析研究使用自举法得出一个稳定的因子结构，以在基于人群的癌症患者样本中识别症状聚类。从省级症状数据库中识别出受试者，并与其他省级数据库相关联。使用样本的随机部分进行验证性因子分析来验证症状聚类，并使用常见的拟合优度标准检查模型拟合。
聚类队列包括 14247 名患者。确定了三个症状聚类：疲劳-疾病症状（疲劳、恶心、嗜睡和呼吸急促）、情绪困扰（抑郁和焦虑）和幸福感差（食欲和幸福感）。这些聚类在队列中的大多数亚群中都很稳定。
使用稳健的统计方法识别常见的症状聚类将有助于确定改善症状管理和识别疾病结局较差风险人群的目标。
