Clinical correlates and prognostic impact of cognitive decline in patients with heart failure and preserved ejection fraction: insights from PARAGON-HF
Bibliographic record
Abstract
Abstract Background Cognitive decline is common in patients with heart failure (HF), but its clinical correlates and prognostic impact are not well understood in this population. Purpose To examine the baseline variables associated with cognitive decline and the associations between cognitive decline and HF outcomes in patients with heart failure and preserved ejection fraction (HFpEF). Methods We analyzed cognitive function, as measured by the Mini-Mental State Examination (MMSE), in 2895 patients with HFpEF enrolled in a pre-specified sub-study of PARAGON-HF. Logistic regression analysis was performed to determine the factors associated with cognitive decline i.e., a decrease in MMSE score of ≥3 points by 48 weeks. Time-updated Cox proportional hazards regressions and semiparametric proportional rates models were used to examine the subsequent risk of clinical outcomes after a decline in MMSE scores of ≥1-, 2- and 3-points during follow-up respectively, adjusted for known prognostic variables including NT-proBNP and baseline MMSE. Results A total of 450 (15.5% of the population) patients experienced a decrease in MMSE score of ≥3 points from baseline, at any follow-up visit. The corresponding number for a decrease in MMSE score of ≥2 points and ≥1 point was 755 (26.1%) and 1231 (42.5%), respectively. Independent predictors of cognitive decline at 48 weeks included older age, living in Latin America or the Asia/Pacific region, ischemic etiology, prior HF hospitalization, history of stroke/TIA, and lower serum albumin level (Figure 1). There was a graded relationship between the size of the decrease in MMSE score from baseline and the subsequent risk of mortality. Notably, a decrease of ≥3 points was associated with a 50% increase in the subsequent risk of death from any cause (adjusted HR 1.53, 95% CI 1.10-2.11) and a 90% elevation in risk of death from cardiovascular causes (1.89, 1.25-2.86), respectively, after extensive adjustment for known prognostic variables and baseline MMSE score. By contrast, cognitive decline during follow-up was not associated with a higher risk of HF hospitalization or the composite of HF hospitalization or CV death (Figure 2). Conclusions In patients with HFpEF, a decline in MMSE score during follow-up is independently associated with a graded increase in the risk for mortality, but not of HF hospitalization.The predictors for cognitive declineRisk of outcomes after a decline in MMSE
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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 | 0.000 |
| 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".