Identification of distinct subgroups of breast cancer patients based on self-reported changes in sleep disturbance. Support Care Cancer

Department of Physiological Nursing, School of Nursing, University of California, San Francisco, CA 94143-0610, USA.
Supportive Care in Cancer (Impact Factor: 2.36). 01/2012; 20(10):2611-9. DOI: 10.1007/s00520-012-1381-3
Source: PubMed


The purposes of this study were to identify distinct subgroups of patients based on self-reported sleep disturbance prior to through 6 months after breast cancer surgery and evaluate for differences in demographic, clinical, and symptom characteristics among these latent classes.
Women (n = 398) who underwent unilateral breast cancer surgery were enrolled prior to surgery. Patients completed measures of functional status, sleep disturbance (i.e., General Sleep Disturbance Scale (GSDS); higher scores indicate higher levels of sleep disturbance), fatigue, attentional fatigue, depressive symptoms, and anxiety prior to surgery and monthly for 6 months.
Three distinct classes of sleep disturbance trajectories were identified using growth mixture modeling. The high sustained class (55.0%) had high and the low sustained class (39.7%) had low GSDS scores prior to surgery that persisted for 6 months. The decreasing class (5.3%) had high GSDS score prior to surgery that decreased over time. Women in the high sustained class were significantly younger, had more comorbidity and poorer function, and were more likely to report hot flashes compared to the low sustained class. More women who underwent mastectomy or breast reconstruction were in the decreasing class. Decreasing and high sustained classes reported higher levels of physical fatigue, attentional fatigue, depressive symptoms, and anxiety compared to the low sustained class.
A high percentage of women has significant sleep disturbance prior to surgery that persists during subsequent treatments (i.e., radiation therapy and chemotherapy). Clinicians need to perform routine assessments and initiate appropriate interventions to improve sleep prior to and following surgery.

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    • "Unconditional GMM with robust maximum likelihood estimation was carried out to identify latent classes with distinct sleep disturbance trajectories using Mplus Version 5.21. These methods are described in detail elsewhere (Van Onselen et al., 2012). In brief, a single growth curve that represented the " average " change trajectory was estimated for the whole sample. "
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