ArticleFrontiers in neurology2022
Risk factors and prediction model of sleep disturbance in patients with maintenance hemodialysis: A single center study.
Article in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 14 citations in OpenAlex.
- An interpretable nomogram for predicting sleep disturbance risk in maintenance hemodialysis patients.International urology and nephrology · 2026Article
- Unmasking factors affecting sleep pattern disturbances among patients undergoing hemodialysis: A cross-sectional study.PloS one · 2026Article
- Global prevalence of poor sleep quality in hemodialysis patients: a systematic review and meta-analysis.Frontiers in medicine · 2026Review
- Determinants of poor sleep quality in hemodialysis patients: a multicenter cross sectional study in Sri Lanka.Frontiers in medicine · 2026Article
- Psychosocial pathways linking family support to sleep disorders in maintenance hemodialysis: the chain mediating role of depression and fatigue.Renal failure · 2025Article
- Article
- Sleep disturbance as a mediator of the relationship between perceived stress and demoralization in hemodialysis patients: a structural equation modeling analysis.Frontiers in psychiatry · 2025Article
- A Risk Correlative Model for Sleep Disorders in Chinese Older Adults Based on Blood Micronutrient Levels: A Matched Case-Control Study.Nutrients · 2024Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: This study aimed to explore the risk factors and develop a prediction model of sleep disturbance in maintenance hemodialysis (MHD) patients. Methods: In this study, 193 MHD patients were enrolled and sleep quality was assessed by Pittsburgh Sleep Quality Index. Binary logistic regression analysis was used to explore the risk factors for sleep disturbance in MHD patients, including demographic, clinical and laboratory parameters, and that a prediction model was developed on the basis of risk factors by two-way stepwise regression. The final prediction model is displayed by nomogram and verified internally by bootstrap resampling procedure. Results: The prevalence of sleep disturbance and severe sleep disturbance in MHD patients was 63.73 and 26.42%, respectively. Independent risk factors for sleep disturbance in MHD patients included higher 0.1 Conclusions: Older age, lower albumin and calcium levels are higher risk factors of sleep disturbance in MHD, and the prediction model for the assessment of sleep disturbance in MHD patients has excellent discrimination and calibration.
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