Prevalence and factors associated with cognitive impairment in Chinese heart failure patients: A pilot study
Bibliographic record
Abstract
Background The prevalence of Cognitive impairment (CI) is high in patients with heart failure (HF). It leads to poor prognosis, such as self-care, hospital readmission and increased mortality. However, such information among Chinese population is unclear. Objective The purpose of this study was to examine the prevalence of CI in Chinese patients with HF, and explore its correlation with biomarkers and clinical factors to better manage HF patients with CI. Methods This study is a cross-sectional study of 200 hospitalized HF patients in China. The cognitive function of HF patients was assessed by the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE). Results The majority are male (62.5%, n = 125), have primary school and below level of education (57.5%, n = 115), NYHA III and above (62%, n = 124). They have an average MoCA score of 15.10 ± 8.18, MMSE score of 19.55 ± 8.23. Age, NYHA class, and atrial fibrillation were independently associated with CI (p < 0.05). There was a significant association between CI and the 4th quartile of TNT (p = 0.013), and the 3rd and 4th quartile of NT-proBNP (p = 0.015, p = 0.038). Conclusions The prevalence of undiagnosed CI in Chinese HF patients is high (81%). HF patients with high levels of TNT or NT-proBNP or both values may be at risk of developing CI. Therefore, we suggest that HF patients with older age, atrial fibrillation, NYHA class II and III, as well as elevated TNT or NT-proBNP or both values to be followed up with a formal evaluation for CI. Nurses need to provide targeted health education program for cognitively impaired HF population to improve their self-care ability and nursing outcome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".