Abstract 8839: Acute Heart Failure: Canadian Perspectives from an RCT and a Registry
Bibliographic record
Abstract
Background/Objectives: Randomized controlled trials (RCT) are limited by their generalizability to the broader non-trial population. The recently completed ASCEND-HF RCT provides the first substantive evaluation of care patterns in Canadian patients with acute heart failure (AHF). In order to provide a context for this RCT we designed a complementary registry at the same RCT sites to characterize practice patterns, baseline characteristics and in-hospital outcomes. Methods: Patients with a principal diagnosis of AHF (ICD-9-CM 402 and 428; ICD-10 I50.x, I11.0, I13.0, I13.2) admitted to 7 hospitals participating in ASCEND-HF (n=697 patients, 2007 to 2010; total of 57 one-month sampling periods) were evaluated and compared to ASCEND-HF RCT patients (n=465, 31 sites, 2007 to 2010). Key inclusion criteria for ASCEND-HF were signs and symptoms of HF, <24 hours from admission to emergency or acute care, and if known, and elevated BNP or LVEF<40%. Values are medians with interquartile range (IQR). Results: Patients in the Registry were older, more likely female and have chronic respiratory disease, less likely to have diabetes, and had a similar incidence of ischemic HF, atrial fibrillation and similar BNP levels.(See Table) Median systolic BP and LVEF was higher in Registry vs. RCT patients. Although Registry patients arrived more often via ambulance, they had longer time from triage to 1 st physician consult but a similar total length of stay as RCT patients. In-hospital mortality was much higher in the Registry compared to the RCT patients: 9.3% vs. 1.3%, p<0.001. Conclusions: Patients in a large RCT are typically younger but have a lower LVEF compared with the patients in a Registry from the same Canadian sites. Despite this, the observed in-hospital mortality is higher in unselected Registry as compared to RCT patients highlighting the need for novel strategies applicable to a generalizable AHF population to improve clinical 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 | 0.237 | 0.381 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".