Abstract 11680: Comorbidity and Cognitive Function Predict Efficiency of a Disease Management Program in Reducing Short-Term Readmission in Heart Failure
Bibliographic record
Abstract
Background: Both comorbidity and cognitive impairment are highly prevalent and predictive of outcomes in heart failure (HF). We sought whether these risk factors could identify patients most likely to benefit from a HF disease management program (DMP) to reduce readmission. Methods: 1363 consecutive HF patients were prospectively followed for 1 month after discharge. Of these, 431 (31%) patients received a DMP (1-month duration, including post-discharge home visits, medication reconciliation, exercise guidance and early clinical review). Primary outcome was all-cause readmission/death at 30-day post-discharge. Charlson comorbidity index (CCI) was calculated based on discharge diagnoses and categorised as mild (CCI≤2), moderate (CCI 3-4) and severe (CCI≥5). Cognitive function was assessed on discharge using the Montreal Cognitive Assessment (MoCA) and classified as normal (MoCA 26-30), low-normal (MoCA 23-25), mildly impaired (MoCA 17-22) and moderate/severely impaired (MoCA≤16). Results: 28% (386/1363) of patients died or were readmitted within 30 days after discharge. Both CCI (relative risk RR=1.06 [95% CI: 1.03, 1.10] per point) and MoCA (RR=0.95 [95% CI: 0.94, 0.96] per point) were significantly predictive of outcome, with a statistically significant interaction (p=0.032). Cross-classification of CCI and MoCA identified a group of high-risk patients (≥mild cognitive impairment and/or severe Charlson comorbidity index) who had a 2.02-fold higher risk of 30-day readmission/death (34%, 309/903) than patients with mild/moderate CCI and without cognitive impairment (17%, 72/426). The effect size of DMP was significantly stronger (p=0.034) among the high-risk patients (RR=0.62 [95% CI: 0.49, 0.77]) compared to low-risk patients (RR=0.98 [95% CI: 0.61, 1.58]). Conclusion: Comorbidities and cognitive impairment strongly predict short-term adverse outcomes in HF and can be used to identify patients who are most likely to benefit from a DMP.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".