San Diego State University/University of California, San Diego Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California, San Diego, San Diego, CA, USA.
Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Support Care Cancer. 2020 Feb;28(2):845-855. doi: 10.1007/s00520-019-04834-w. Epub 2019 Jun 3.
Sleep disturbance and cancer-related fatigue (CRF) are among the most commonly reported symptoms associated with breast cancer and its treatment. This study identified symptom cluster groups of breast cancer patients based on multidimensional assessment of sleep disturbance and CRF prior to and during chemotherapy.
Participants were 152 women with stage I-IIIA breast cancer. Data were collected before chemotherapy (T1) and during the final week of the fourth chemotherapy cycle (T2). Latent profile analysis was used to derive groups of patients at each timepoint who scored similarly on percent of the day/night asleep per actigraphy, the Pittsburgh Sleep Quality Index global score, and the five subscales of the Multidimensional Fatigue Symptom Inventory-Short Form. Bivariate logistic regression evaluated if sociodemographic/medical characteristics at T1 were associated with group membership at each timepoint.
Three groups (Fatigued with sleep complaints, Average, Minimal symptoms) were identified at T1, and five groups (Severely fatigued with poor sleep, Emotionally fatigued with average sleep, Physically fatigued with average sleep, Average, Minimal symptoms) at T2. The majority of individuals in a group characterized by more severe symptoms at T1 were also in a more severe symptom group at T2. Sociodemographic/medical variables at T1 were significantly associated with group membership at T1 and T2.
This study identified groups of breast cancer patients with differentially severe sleep disturbance and CRF symptom profiles prior to and during chemotherapy. Identifying groups with different symptom management needs and distinguishing groups by baseline sociodemographic/medical variables can identify patients at risk for greater symptom burden.
睡眠障碍和与癌症相关的疲劳(CRF)是与乳腺癌及其治疗相关的最常见报告症状之一。本研究通过在化疗前和化疗期间对睡眠障碍和 CRF 进行多维评估,确定了乳腺癌患者的症状聚类组。
参与者为 152 名 I 期至 IIIA 期乳腺癌女性。数据在化疗前(T1)和第四次化疗周期的最后一周(T2)收集。使用潜在剖面分析在每个时间点上获得相似得分的患者组,这些得分是基于活动记录仪上白天/夜晚睡眠时间百分比、匹兹堡睡眠质量指数总体得分和多维疲劳症状清单-短表的五个子量表得出的。双变量逻辑回归评估 T1 时的社会人口学/医学特征是否与每个时间点的组内成员身份相关。
在 T1 时确定了三个组(有睡眠问题的疲劳组、平均组、最小症状组),在 T2 时确定了五个组(睡眠差的严重疲劳组、情绪疲劳的平均睡眠组、身体疲劳的平均睡眠组、平均组、最小症状组)。在 T1 时具有更严重症状特征的组中的大多数个体在 T2 时也处于更严重的症状组中。T1 时的社会人口学/医学变量与 T1 和 T2 时的组内成员身份显著相关。
本研究在化疗前和化疗期间确定了具有不同严重程度睡眠障碍和 CRF 症状特征的乳腺癌患者组。确定具有不同症状管理需求的组,并根据基线社会人口学/医学变量区分组,可以识别出症状负担更大的患者。