Association between mild cognitive impairment and sleep quality in patients with chronic heart failure: a cross-sectional study
Bibliographic record
Abstract
Objective: Mild cognitive impairment (MCI) has increasingly been recognized as a significant comorbidity in patients with chronic heart failure (CHF), adversely affecting prognosis and quality of life, despite limited research examining the role of sleep quality in this relationship. This study aimed to assess the prevalence of MCI and poor sleep quality in patients with CHF and to examine the association between them. Methods: We conducted a cross-sectional study among 329 patients with CHF recruited from a hospital in Nanning, China, between September 2024 and June 2025. We collected the sociodemographic and clinical characteristics from all participants using a general information questionnaire. We assessed global cognitive function with the Beijing version of the Montreal Cognitive Assessment Scale (MoCA-BJ) and evaluated subjective sleep quality over the preceding one-month period using the Pittsburgh Sleep Scale Index (PSQI). We examined the association between MCI and sleep quality using point-biserial correlation coefficient analysis, and then further assessed it with hierarchical regression models, adjusting for potential confounders. Results: < 0.01). Multivariable analysis demonstrated that sleep quality remained independently associated with MCI after adjusting for other risk factors, with the final model explaining over half of the variance in MCI risk. Conclusion: Poor sleep quality shows a strong independent association with MCI in CHF patients. These findings highlight the importance of sleep assessment in CHF management and suggest that addressing poor sleep quality may represent a valuable approach in comprehensive care strategies aimed at preserving cognitive function in this CHF population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".