Severity of functional impairments by race and sex in older patients hospitalized with acute decompensated heart failure
Bibliographic record
Abstract
BACKGROUND: Older patients hospitalized with acute decompensated heart failure (ADHF) have marked functional impairments, which may contribute to their delayed and incomplete recovery and persistently poor outcomes. However, whether impairment severity differs by race and sex is unknown. METHODS: REHAB-HF trial participants (≥60 years) were assessed just before discharge home from ADHF hospitalization. Physical function [Short Physical Performance Battery; 6-min walk distance (6MWD)], frailty (Fried criteria), cognition [Montreal Cognitive Assessment (MoCA)], quality-of-life [Kansas City Cardiomyopathy Questionnaire, Short-Form-12, EuroQol-5D-5L], and depression [Geriatric Depression Scale (GDS)] were examined by race and sex. RESULTS: This prespecified subgroup cross-sectional analysis included 337 older adults (52% female, 50% Black). Black participants were on average younger than White participants (70.3 ± 7.2 vs. 74.7 ± 8.3 years). After age, body mass index, ejection fraction, comorbidity, and education adjustment, and impairments were similarly common and severe across groups except: Black male and Black and White female participants had more severely impaired walking function compared with White male participants [6MWD (m) 187 ± 12, 168 ± 9170 ± 11 vs. 239 ± 9, p < 0.001]; gait speed (m/s) (0.61 ± 0.03, 0.56 ± 0.02, 0.55 ± 0.02 vs. 0.69 ± 0.02, p < 0.001); White female participants had the highest frailty prevalence (72% vs. 47%-51%, p = 0.007); and Black participants had lower MoCA scores compared with White participants (20.9 ± 4.5 vs. 22.8 ± 3.9, p < 0.001). Depressive symptoms were common overall (43% GDS ≥5), yet underrecognized clinically (18%), especially in Black male participants compared with White male participants (7% vs. 20%). CONCLUSION: Among older patients hospitalized for ADHF, frailty and functional impairments with high potential to jeopardize patient HF self-management, safety, and independence were common and severe across all race and sex groups. Impairment severity was often worse in Black participant and female participant groups. Formal screening across frailty and functional domains may identify those who may require greater support and more tailored care to reduce the risk of adverse events and excess hospitalizations and death.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".