Fifteen-Year Differences in Indications for Cardiac Resynchronization Therapy in International Guidelines—Insights from the Heart Failure Registries of the European Society of Cardiology
Bibliographic record
Abstract
Cardiac resynchronization therapy (CRT) applied to selected patients with heart failure (HF) improves their prognosis. In recent years, eligibility criteria for CRT have regularly changed. This study aimed to investigate the changes in eligibility of real-life HF patients for CRT over the past fifteen years. We reviewed European and North American guidelines from this period and applied them to HF patients from the ESC-HF Pilot and ESC-Long-Term Registries. Taking into consideration the criteria assessed in this study (including all classes of recommendations i.e., class I, IIa and IIb, as well as patients with AF and SR), the 2013 (ESC) guidelines would have qualified the most patients for CRT (266, 18.3%), while the 2015 (ESC) guidelines would have qualified the least (115, 7.9%; p-value for differences between all analyzed papers <0.0001). There were only 26 patients (1.8%) who would be eligible for CRT using the class I recommendations across all of the guidelines. These results demonstrate the variability in recommendations for CRT over the years. Moreover, this data indicates underuse of this form of pacing in HF and highlights the need for more studies in order to improve the outcomes of HF patients and further personalize their management.
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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 | 0.020 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".