Patient-Reported Status and Heart Failure Outcomes in Asia by Sex, Ethnicity, and Socioeconomic Status
Bibliographic record
Abstract
In heart failure (HF), symptoms and health-related quality of life (HRQoL) are known to vary among different HF subgroups, but evidence on the association between changing HRQoL and outcomes has not been evaluated. The authors sought to investigate the relationship between changing symptoms, signs, and HRQoL and outcomes by sex, ethnicity, and socioeconomic status (SES). Using the ASIAN-HF (Asian Sudden Cardiac Death in Heart Failure) Registry, we investigated associations between the 6-month change in a “global” symptoms and signs score (GSSS), Kansas City Cardiomyopathy Questionnaire overall score (KCCQ-OS), and visual analogue scale (VAS) and 1-year mortality or HF hospitalization. In 6,549 patients (mean age: 62 ± 13 years], 29% female, 27% HF with preserved ejection fraction), women and those in low SES groups had higher symptom burden but lower signs and similar KCCQ-OS to their respective counterparts. Malay patients had the highest GSSS (3.9) and lowest KCCQ-OS (58.5), and Thai/Filipino/others (2.6) and Chinese patients (2.7) had the lowest GSSS scores and the highest KCCQ-OS (73.1 and 74.6, respectively). Compared to no change, worsening of GSSS (>1-point increase), KCCQ-OS (≥10-point decrease) and VAS (>1-point decrease) were associated with higher risk of HF admission/death (adjusted HR: 2.95 [95% CI: 2.14-4.06], 1.93 [95% CI: 1.26-2.94], and 2.30 [95% CI: 1.51-3.52], respectively). Conversely, the same degrees of improvement in GSSS, KCCQ-OS, and VAS were associated with reduced rates (HR: 0.35 [95% CI: 0.25-0.49], 0.25 [95% CI: 0.16-0.40], and 0.64 [95% CI: 0.40-1.00], respectively). Results were consistent across all sex, ethnicity, and SES groups (interaction P > 0.05). Serial measures of patient-reported symptoms and HRQoL are significant and consistent predictors of outcomes among different groups with HF and provide the potential for a patient-centered and pragmatic approach to risk stratification.
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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) | 0.000 | 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 | 0.001 |
| 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".