Cognitive impairment and evolution of heart failure: a heart-brain interconnection
Bibliographic record
Abstract
Abstract Aims While many studies have suggested that heart failure (HF) may lead to cognitive impairment (CI), our understanding about this relationship is limited. This study investigated the association of cognitive function with HF risk factors and how CI may impact the development of incident clinical HF in people with subclinical HF. Methods and Results People with either preclinical (at risk and asymptomatic, n=814) or clinical HF (symptomatic, n=1152) were recruited from communities, clinics, and hospitals in five Australian States (Victoria, New South Wales, South Australia, Tasmania, and Queensland). CI was measured with Montreal Cognitive Assessment (MOCA <26). Left ventricular dysfunction (LVD) was assessed as global longitudinal strain (GLS <16%). Those with preclinical HF were followed-up for 45±13 months for incident clinical HF or death. Baseline MOCA was independently associated with age, HF stage, diabetes, atrial fibrillation, chronic lung disease, cerebrovascular disease, GLS, left atrial volume index, and left ventricular filling pressure. CI significantly increased the associations of age (interaction p<0.001), comorbidity index (interaction<0.001), and GLS (interaction p=0.042) with clinical HF. Of those with preclinical HF at baseline, 71 (9%) developed clinical HF and 87 (11%) died within the follow-up period. In time-to-event analysis of participants with preclinical HF, those with either CI or LVD had double the risk of developing clinical HF, compared with those with normal cognition and LV function. Those with concomitant CI and LVD had a 4-fold greater risk of developing HF (SHR=4.01 [95% CI: 2.39-6.76]). Conclusions CI is associated with increased risk of incident clinical HF, independent of cardiac function and other HF risk factors.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".