Department of Physiological Nursing, School of Nursing, University of California, San Francisco, CA.
Department of Physiological Nursing, School of Medicine, Stanford University, Stanford, CA.
Sleep. 2019 Oct 9;42(10). doi: 10.1093/sleep/zsz151.
Purposes of this study were to identify subgroups of patients with distinct sleep disturbance profiles and to evaluate for differences in demographic, clinical, and various sleep characteristics, as well for differences in the severity of co-occurring symptoms among these subgroups.
Outpatients with breast, gynecological, gastrointestinal, or lung cancer (n = 1331) completed questionnaires six times over two chemotherapy cycles. Self-reported sleep disturbance was evaluated using the General Sleep Disturbance Scale (GSDS). Latent profile analysis was used to identify distinct subgroups.
Three latent classes with distinct sleep disturbance profiles were identified (Low [25.5%], High [50.8%], Very High [24.0%]) across the six assessments. Approximately 75% of the patients had a mean total GSDS score that was above the clinically meaningful cutoff score of at least 43 across all six assessments. Compared to the Low class, patients in High and Very High classes were significantly younger, had a lower functional status, had higher levels of comorbidity, and were more likely to be female, more likely to have childcare responsibilities, less likely to be employed, and less likely to have gastrointestinal cancer. For all of the GSDS subscale and total scores, significant differences among the latent classes followed the expected pattern (Low < High < Very High). For trait and state anxiety, depressive symptoms, morning and evening fatigue, decrements in attentional function, and decrements in morning and evening energy, significant differences among the latent classes followed the expected pattern (Low < High < Very High).
Clinicians need to perform in-depth assessments of sleep disturbance and co-occurring symptoms to identify high-risk patients and recommend appropriate interventions.
本研究旨在确定具有不同睡眠障碍特征的患者亚组,并评估这些亚组之间在人口统计学、临床和各种睡眠特征方面的差异,以及共病症状严重程度的差异。
1331 名患有乳腺癌、妇科、胃肠道或肺癌的门诊患者在两个化疗周期内完成了六次问卷调查。使用一般睡眠障碍量表(GSDS)评估自我报告的睡眠障碍。采用潜在剖面分析识别不同的亚组。
在六次评估中,确定了三个具有不同睡眠障碍特征的潜在亚组(低[25.5%]、高[50.8%]、非常高[24.0%])。大约 75%的患者在所有六次评估中,平均总 GSDS 得分均高于至少 43 的临床有意义临界值。与低分组相比,高分组和非常高分组的患者年龄更小,功能状态更低,合并症水平更高,更有可能是女性,更有可能有育儿责任,更不可能就业,更不可能患有胃肠道癌。对于所有 GSDS 子量表和总分,潜在亚组之间存在显著差异,符合预期模式(低<高<非常高)。对于特质和状态焦虑、抑郁症状、早晚疲劳、注意力功能下降以及早晚能量下降,潜在亚组之间存在显著差异,符合预期模式(低<高<非常高)。
临床医生需要对睡眠障碍和共病症状进行深入评估,以识别高风险患者并推荐适当的干预措施。