P4515The aging heart failure patient: frailty and cognitive impairment more common than you would expect - baseline data of the heart-brain clinic
Bibliographic record
Abstract
Abstract Background Heart failure (HF) is a cardiovascular disease that is increasing by epidemic proportions, largely due to an aging society and therapeutic advances in disease management. Because heart failure is largely a cardiogeriatric syndrome, age-related syndromes such as frailty and cognitive impairment are common in heart failure patients. Purpose To assess the prevalence and determinants of frailty and cognitive impairment in a HF population ≥60 years of age. Methods Data from n=236 patients with HF (77±9 years; 43% female) visiting the heart-brain clinic in Amsterdam in 2018–2019. HF severity was evaluated by NT-proBNP and NYHA-classification. Frailty was assessed using Fried's frailty criteria, cognition using the Montreal cognitive assessment (MoCa). Logistic regression analyses were performed to evaluate which variables were associated with frailty and cognitive impairment. Results Median (IQR) NT-proBNP was 2000 (876–3469) pmol/L, 38% of patients had NYHA III-IV. 51% of patients were pre-frail and 28% frail. 77% of the patients were (mildly) cognitive impaired. Age, NYHA-classification III-IV, NT-proBNP>2000 pmol/L and use of ≥10 drugs were associated with frailty; HR (95% CI): 2.0 (1.4–3.0) per 10 years, 3.4 (1.9–6.2), 1.8 (1.0–3.2) and 1.8 (1.4–3.3) respectively. Age was associated with cognitive impairment; HR (95% CI) 2.2 (1.4–3.6) per 10 years. Figure 1 Conclusion(s) Frailty affects almost a third of the patients with HF and is more prevalent in older patients and those with more severe HF. Screening for frailty and cognitive impairment should be part of the standard workup in older HF patients as frail and/or cognitively impaired HF patients are less likely to adhere to their HF treatment and more likely to be (re)admitted to hospital for HF.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".