Abstract 15446: Impact of Patient Demographics on Optimal Beta-Blocker Use After New-Onset Heart Failure: Analysis of the HF-OPT Study (Heart Failure Optimization Study)
Bibliographic record
Abstract
Background: While beta-blockers (BB) have helped to improved outcomes in patients with heart failure and a low ejection fraction (HFrEF), the titration to target doses has been suboptimal and it is not clearly understood why. The Heart Failure Optimization Study (HF-OPT) study in newly diagnosed HF patients (pts) collected baseline data, arrhythmias during wearable cardioverter defibrillator (WCD), as well as medications and dosing. Purpose: The objective was to assess which factors were associated with receiving BB at target doses by day 180. Methods: Data from pts with newly diagnosed HFrEF during WCD use was collected from 487 pts (54% from US, 72% male, age 59±13) in the HF-OPT study. Target dose of BB were defined as the lowest maximum dose from either the ESC or ACC/AHA/HFSA guidelines. Supraventricular tachycardia (SVT) was used to label all dysrhythmias originating at or above the atrioventricular node and captured by the WCD. Association between BB and other variables were assessed using logistic regression. Results: 6 months after a new diagnosis of HFrEF, 97% of pts were prescribed BB, but only 25% were on target doses. Univariate analysis showed that only BMI, age, sex, black race, a history of hypertension, and no history of PCI or angina, were associated with receiving target doses of BBs by 180 days. Having episodes of SVT during the first 180 days were also associated with receiving target BB doses (Table). In multivariate analysis, all were significantly associated with target BB dose except BMI, SVT during WCD use and history of PCI. Conclusions: The full beneficial effects of BB may not be achieved in patients with HFrEF if the patients are not recieving target doses. In the HF-OPT study, factors positively associated with reaching target BB doses included younger age, black race, being male, and a history of hypertension. Having a history of angina was associated with not achieving target BB doses. The significance of these differences should be further explored.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".