Cognitive impairment as a determinant of response to management plans after heart failure admission
Bibliographic record
Abstract
AIMS: Cognitive impairment (CI) is highly prevalent in heart failure (HF), and increases patients' risks of readmission. This study sought to determine whether the presence and degree of CI could identify patients most likely to benefit from a HF disease management programme (DMP) to reduce readmissions. METHODS AND RESULTS: A total of 1152 consecutive Australian patients admitted with HF (2014-2017) were prospectively followed up for 12 months. Of these, 324 patients who received DMP (1-month duration, including post-discharge home visits, medication reconciliation, exercise guidance and early clinical review) were matched (1:2 ratio) with 648 usual care patients. Cognitive function was assessed either on the day of or one day before discharge using the Montreal Cognitive Assessment (MoCA). Outcomes included readmission or death at 1, 3 and 12 months, and days at home within 12 months of discharge. Poorer cognitive function was associated with all adverse outcomes. Compared with usual care, DMP was associated with lower odds of 30-day [odds ratio (OR) 0.60, 95% confidence interval 0.40, 0.91] and 90-day (OR 0.53, 95% confidence interval 0.36, 0.77) readmission or death, and with 19 more days at home within 12 months, independent of HF therapy. The effect sizes of these associations were greater for patients with diminished cognition than those with normal cognition (interaction P = 0.036), and might have been more pronounced among those with mild CI compared with those with more severe CI (MoCA score 17-22; OR 0.42, 95% confidence interval 0.21, 0.87) at 30 days (OR 0.31, 95% confidence interval 0.16, 0.60 at 90 days). Patients with normal cognition had fewer events, irrespective of DMP. CONCLUSIONS: Cognitive function may determine how HF patients respond to a DMP. Cognitive screening before implementation of a DMP may allow personalized plans for patients with different levels of cognitive function.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".