Cardiac Resynchronization Therapy for Enabling Guideline-Directed Medical Therapy Optimization in Heart Failure
Bibliographic record
Abstract
AIMS: We aimed to assess whether cardiac resynchronization therapy (CRT) might serve as an enabler for guideline-directed medical therapy (GDMT) optimization. METHODS AND RESULTS: Patients with heart failure with reduced ejection fraction (HFrEF) enrolled in the Swedish Heart Failure Registry between January 2009 and August 2022 were considered. Patients receiving a CRT close to the index registration were the cases, whereas controls had not received a CRT despite having an indication. Overall, 1543 (25%) HFrEF cases and 4537 (75%) controls were analysed in the intention-to-treat analysis. At baseline, beta-blockers, angiotensin-converting enzyme inhibitor (ACEi), angiotensin receptor blocker (ARB) or angiotensin receptor-neprilysin inhibitor (ARNi), mineralocorticoid receptor antagonist (MRA) and loop diuretic use was 84% versus 86%, 89% versus 88%, 57% versus 46% and 62% versus 59% in patients receiving versus not receiving CRT, respectively. At 1.5-year follow-up, patients receiving a CRT more likely experienced an improved use/dose of beta-blocker therapy (46% vs. 35%) and decreased loop diuretic use/dose (30% vs. 24%) versus controls. These associations were consistent after adjustments (odds ratio [OR] 1.83, 95% confidence interval [CI] 1.58-2.13, and OR 1.26, 95% CI 1.07-1.48, respectively), and confirmed in the per-protocol analysis (i.e. after excluding controls who received a CRT during follow-up). A significant association between CRT and the likelihood of ACEi/ARB/ARNi and MRA optimization (OR 1.22, 95% CI 1.04-1.44, and OR 1.25, 95% CI 1.05-1.50, respectively) was observed in the per-protocol analysis. CONCLUSIONS: In this large nationwide real-world population with HFrEF, CRT implantation was associated with enabled use/dose of heart failure GDMT and decreased loop diuretic need (use/dose).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".