Cognitive Domains and Postdischarge Outcomes in Hospitalized Patients With Heart Failure
Bibliographic record
Abstract
Background Cognitive impairment is a prevalent, independent marker of readmission in heart failure (HF), but the screening is time-consuming. This study sought (1) to identify HF patients at low risk of cognitive impairment (obviating screening) and (2) to simplify a predictive model of HF outcomes by only using cognitive domains that are most predictive. Methods and Results The Montreal Cognitive Assessment was performed in 1152 Australian patients with HF who were followed for 12 months. One-third (376/1152) of the patients were enrolled into an HF disease management plan to reduce early readmission. Postdischarge outcomes in HF included 30- and 90-day readmission or death and days alive and out of hospital within 12 months of discharge. Cognitive impairment-present in 54% of patients-independently predicted HF outcomes. Normal cognition could be predicted with common clinical and sociodemographic factors with good discrimination (C statistic=0.74 [0.69-0.78]). The visuospatial/executive and orientation domains were most predictive of HF postdischarge outcomes. Using either Montreal Cognitive Assessment score or these 2 domains provided similar incremental values ( P=0.0004 and P=0.0008, respectively) in predicting HF outcomes (both C statistic=0.76) and could similarly identify a group of high-risk patients who benefited most from an HF disease management plan. Conclusions Cognitive function independently predicts HF outcomes and may also contribute to how a patient responds to intervention. The time and resources spent on cognitive assessment for risk-stratification in HF may be minimized by (1) identifying patients with low risk of cognitive impairment and (2) simplifying the screening instrument to include only the domains that are most predictive of postdischarge outcomes in 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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".