Distinct Sleep Quality Profiles and Their Varying Levels of Stigma Among Patients Undergoing Maintenance...

To identify distinct sleep quality profiles among patients undergoing maintenance hemodialysis (MHD) using latent profile analysis (LPA), and examine differences in perceived stigma across these sleep quality subtypes.

From December 2024 to March 2025, a total of 334 MHD patients were recruited via convenience sampling from the nephrology departments of two tertiary hospitals in Xinjiang, China. Data were collected using structured questionnaires, including the Pittsburgh Sleep Quality Index (PSQI), the Self-Rating Depression Scale (SDS), and the Social Impact Scale (SIS), along with sociodemographic and clinical information. LPA was employed to identify latent subgroups of sleep quality based on PSQI components. Multinomial logistic regression was used to determine predictors of sleep profile membership. Differences in stigma scores across sleep profiles were analyzed using non-parametric equivalents.

Three distinct sleep profiles were identified: Class 1 - "overall better sleep", Class 2 - "short sleep duration and low efficiency", and Class 3 - "poor sleep quality with high medication use". Multinomial logistic regression identified comorbid heart failure (OR=2.867, P=0.001 for Class 2), pruritus (OR=2.715, P<0.001 for Class 2), depressive symptoms (OR=2.568, P=0.001 for Class 2; OR=4.823, P<0.001 for Class 3), and elevated C-reactive protein (OR=1.044, P<0.001 for Class 2; OR=1.035, P=0.008 for Class 3) as significant predictors of poorer sleep profiles. Stigma scores differed significantly across all sleep profiles (Class 1 vs 2: P=0.039; Class 1 vs 3: P<0.001; Class 2 vs 3: P=0.005), with Class 3 exhibiting the highest median SIS score.

Patients with MHD exhibit heterogeneous patterns of sleep disturbance, which are associated with varying levels of perceived stigma. Those with the poorest sleep quality and highest reliance on medication experience the most pronounced stigma. Tailored interventions addressing sleep-related issues and psychosocial factors may help reduce stigma and improve patient well-being.