Comparison of Proxy and Self-Reported Functional Ability in Heart Failure Patients with Cognitive Impairment
Bibliographic record
Abstract
Background: Heart failure (HF) patients often experience cognitive impairment that negatively impacts self-management ability, predisposing these individuals to worse post-hospitalization outcomes. Patient proxies may have more insight into a patient's self-management capability especially in the context of patient cognitive impairment. Here, we incorporate proxy input to evaluate associations between patient- and proxy-reported capacity for instrumental activities of daily living (IADL) stratified along a patient's cognitive function in an older hospitalized heart failure population. Methods: We conducted a quality improvement study in older HF inpatients with cognitive impairment determined by Mini-Cog. Functional activity performance has been previously assessed using the Assessment of Living Skills and Resources Revision 2 (ALSAR), a validated index where higher numerical scores associate with increasing dependence in completing IADL and risk for needing a more structured living environment, nursing home placement, hospitalization, and death. We assessed ALSAR with patient self-report and proxy-report (range 0-44 lower scores equate to better performance) and calculated the absolute difference (ALSAR difference, lower scores show stronger agreement between patients and proxies). Patients' Montreal Cognitive Assessment (MoCA) scores, among which scores less than 26 suggest clinically significant cognitive impairment, were correlated with ALSAR difference. Results: Median patient age was 74 years. Forty-two percent were female among our sample of 30 hospitalized HF patients with cognitive impairment. Median patient ALSAR score of 4 (range 2-7) differed from median proxy ALSAR score of 7 (range 4-12) (p<0.01). Lower MoCA correlated with higher ALSAR difference (r=-0.58, p<0.01). Conclusions: Assessing ALSAR difference in clinical practice is feasible and it correlates to MoCA score in hospitalized HF patients with cognitive impairment, consistent with prior work. These results support the notion that proxy-input of patient IADL assessment could improve patient needs detection among HF patients with cognitive impairment. This information may enhance risk assessment, disease management and discharge planning when targeting and proactively involving proxies. Prospective studies should evaluate this novel metric and its association with patient-centered outcomes.
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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 | 0.003 | 0.010 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".