P4188Cognitive impairment in the elderly people with preserved ejection fraction is related to the reduced peak exercise stroke volume
Bibliographic record
Abstract
Aims: Severity and prognosis of heart failure (HF) in the elderly are mostly dependent on comorbidities, nutrition, and frailty. Among them, cognitive impairment is of a great importance because it leads to poor self-care and poor drug-adherence, and leads to frequent hospital admissions. We reported that 66.7% of elderly patients (mean age, 85.1 years) who were admitted to our hospital had cognitive impairment, and that cognitive function in the old HF patients was affected by cardiac diastolic dysfunction. This study aimed to investigate the relationship between cognitive function and cardiac function in community dwelling people with preserved ejection fraction. Methods and results: Subjects were 108 Japanese community dwelling older adults (25 men and 83 women; mean age, 74.7 years). Cardiac functional parameters at rest were assessed with brain natriuretic peptide and echocardiography. The cardiopulmonary exercise test was used to test these parameters during exercise. Cognitive function was assessed with the Japanese version of the Montreal Cognitive Assessment (MoCA-J). Left ventricular ejection fraction was 67.7% (50–80). There were significant correlations between MoCA-J score and age (r=−0.388), peak VO2 (r=0.201), peak VO2/HR (r=0.243), peak VO2/weight (r=0.244), peak metabolic equivalents (r=0.244), usual walking speed (r=−0.200), and the Timed Up and Go test (r=−0.230). Multiple linear regression analysis after adjusting for potential confounders showed that peak VO2/HR was an independent determinant of MoCA-J score. Moreover, after 6 months of exercise training in 64 subjects we found that the percent change of peak VO2/HR was related to the percent change of MoCA-J score (r=0.296).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 0.001 |
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".