Mild cognitive impairment is associated with subclinical diastolic dysfunction in patients with chronic heart disease
Bibliographic record
Abstract
Background: To examine mild cognitive impairment and its associations with subclinical cardiac dysfunction in patients with chronic heart disease yet to develop the clinical syndrome of chronic heart failure (CHF). Methods and results: Patients from the Nurse-led Intervention for Less Chronic Heart Failure Study (n = 373 with chronic heart disease other than CHF; 64 ± 11 years, 69% men) were screened for mild cognitive impairment [Montreal cognitive assessment (MoCA) score <26] and underwent echocardiographic/clinical profiling. We investigated associations of mild cognitive impairment and MoCA cognitive domain subscores with global cardiac status ('normal' vs. 'diastolic dysfunction' vs. 'other cardiac abnormality') and individual echocardiographic parameters. Patients with mild cognitive impairment (n = 161; 43%) demonstrated a higher age-adjusted prevalence of diastolic dysfunction (37% vs. 24%; P < 0.05). Multivariate logistic regression (adjusted for age, sex, and other relevant clinical factors) indicated that the odds of mild cognitive impairment were two-times higher with diastolic dysfunction (P = 0.030) and 1.7-times higher with 'other cardiac abnormalities' (P = 0.082) vs. normal cardiac status. In turn, mild cognitive impairment was predicted by left-ventricular (LV) filling pressure (based on the ratio of early diastolic filling and annular velocities; adjusted odds ratio 1.07 per unit increase, P = 0.022), but not LV structural parameters. Specific deficits in the cognitive domains of executive functioning and visuo-constructional abilities were also independently predicted by diastolic dysfunction (P < 0.05). Conclusion: Mild cognitive impairment is prevalent in patients with subclinical chronic heart disease at high-risk of CHF. Independent associations with LV diastolic dysfunction suggest a link between cardiac and cognitive functioning beyond shared risk factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".