Rapid Response Radiotherapy Program, Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada.
Palliat Med. 2012 Sep;26(6):826-33. doi: 10.1177/0269216311420197. Epub 2011 Aug 24.
Advanced cancer patients often experience multiple concurrent symptoms, which can have prognostic effects on patients' quality of life. Including patients who did not experience all of the symptoms measured by an assessment tool may interfere with accurate symptom cluster identification. Varying statistical methods may also contribute to inconsistencies of cluster results.
To compare symptom clusters in a subgroup of patients reporting exclusively non-zero ESAS scores with those in the total patient sample. To examine whether using different statistical methods results in varied symptom clusters.
Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and Exploratory Factor Analysis (EFA) were performed on the 'non-zero' subgroup and the total patient sample to identify symptom clusters at baseline and weeks 1, 2, 4, 8 and 12 following palliative radiotherapy.
SETTING/PARTICIPANTS: A previous single-centre study used Principal Component Analysis to explore symptom clusters in 1296 advanced cancer patients. The present study analyzed this previously reported data set.
Notably different symptom clusters were extracted between the two patient groups regardless of the statistical method at baseline, with the exception of a cluster composed of drowsiness, fatigue and dyspnea using Principal Component Analysis and Hierarchical Cluster Analysis. At follow-ups, different statistical methods yielded significantly varied symptom clusters. Only anxiety, depression and well-being consistently occurred in the same cluster across methods and over time.
The composition of symptom clusters varied depending on if patients with non-zero scores were excluded at baseline and on the statistical method employed. Identifying valid clusters may prove useful for bettering symptom diagnosis and management for cancer patients.
晚期癌症患者常同时出现多种症状，这些症状可能对患者的生活质量产生预后影响。如果评估工具未测量到所有症状，而纳入部分患者，可能会干扰对症状群的准确识别。不同的统计方法也可能导致聚类结果不一致。
比较报告仅有非零 ESAS 评分的患者亚组中的症状群与总患者样本中的症状群。检查使用不同的统计方法是否会导致不同的症状群。
对“非零”亚组和总患者样本进行主成分分析（PCA）、层次聚类分析（HCA）和探索性因子分析（EFA），以确定在姑息性放疗后第 1、2、4、8 和 12 周时基线和随访时的症状群。
设置/参与者：先前的单中心研究使用主成分分析探讨了 1296 例晚期癌症患者的症状群。本研究分析了之前报道的数据集中。
无论使用何种统计方法，基线时两个患者组之间都提取了明显不同的症状群，除了使用主成分分析和层次聚类分析时由嗜睡、疲劳和呼吸困难组成的一个群外。在随访时，不同的统计方法产生了显著不同的症状群。只有焦虑、抑郁和幸福感在不同方法和不同时间始终出现在同一群中。
症状群的组成取决于是否在基线时排除了非零评分的患者，以及所使用的统计方法。识别有效的症状群可能有助于改善癌症患者的症状诊断和管理。
