Analysis of potential categories of sleep problems in non-dialysis patients with chronic kidney disease.

Abstract

Introduction: Sleep disturbances are highly prevalent among patients with non-dialysis chronic kidney disease (CKD) and are associated with adverse clinical outcomes. However, current research predominantly relies on aggregate scale scores, which may overlook the heterogeneity of sleep symptoms. This study aims to identify distinct latent categories of sleep problems and their influencing factors among patients with non-dialysis CKD using latent class analysis, thereby providing an evidence base for phenotype-specific interventions.

Methods: From June to July 2023, a convenience sampling was used to select 405 patients from the Nephrology Department of a tertiary hospital in Hangzhou. Data were collected using a general information questionnaire, the Pittsburgh Sleep Quality Index (PSQI), the Hospital Anxiety and Depression Scale (HADS), the International Restless Legs Syndrome Assessment Scale (IRLS), and a Visual Analog Scale (VAS). Mplus 8.0 was used for latent class analysis, and unordered multinomial logistic regression analysis was performed to evaluate factors associated with the different latent classes.

Results: Three latent classes were identified: "inefficient sleep and short sleep duration" (34.6%), "good sleep" (50.4%), and "low sleep quality with long sleep duration" (15.0%). Multinomial logistic regression analysis revealed that, compared with the good sleep group, age ≥45 years, skin pruritus, edema, early CKD stage, glucocorticoid or hypnotic use, anxiety and depression, and a history of COVID-19 infection were significant factors associated with sleep problem classification (P < 0.05).

Conclusion: Sleep problems among patients with non-dialysis CKD are heterogeneous. Targeted, class-specific interventions should be developed to improve sleep quality for different patient subgroups.