Abstract 9541: Comparing Patient Reported Outcomes and New York Heart Association Class Among Patients Hospitalized for Heart Failure
Bibliographic record
Abstract
Background: The New York Heart Association (NYHA) classification remains a cornerstone nomenclature for quantifying health status of patients with heart failure (HF). How this clinician-reported classification compares with patient-reported outcomes among patients hospitalized for HF is unclear. Methods: ASCEND-HF was a global randomized trial comparing nesiritide versus placebo among patients hospitalized for HF, irrespective of EF. Among patients with complete baseline data for NYHA class and the patient-reported EuroQOL-5 dimensions (EQ-5D), each scale was organized into 4 levels (NYHA Class I-IV; EQ-5D utility index [UI] 0.75-1.00, 0.50-0.74, 0.25-0.49, <0.25 and EQ-5D visual analogue scale [VAS] 75-100, 50-74, 25-49, <25). Levels of NYHA class and EQ-5D were then mapped to each other from “best” to “worst.” Minor and moderate-severe discordance were defined as NYHA class and EQ-5D differing by 1 level and ≥2 levels, respectively. Factors associated with moderate-severe discordance were then assessed via backwards selection multivariable regression. Results: Among 5,741 patients, concordance, minor and moderate-severe discordance between NYHA class and EQ-5D UI occurred in 22%, 40% and 38% of patients, respectively. For EQ-5D VAS, this categorization occurred in 29%, 48% and 23%. Discordance was more often due to worse NYHA class. Patients with moderate-severe discordance were more likely to have worse NYHA class and higher EQ-5D scores. NYHA Class IV, higher EQ-5D scores, race and geographic region were among multiple patient factors independently associated with moderate-severe discordance (Figure 1). Conclusions: In patients hospitalized for HF, the majority exhibited discordance between the clinician-reported NYHA class and patient-reported health status. Discordance was more often due to worse NYHA class. Multiple patient factors were independently associated with higher likelihood of moderate-severe discordance.
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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 | 0.006 | 0.009 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".