Impact of Left Bundle Branch Block on Heart Rate and its Relationship to Treatment with Ivabradine in Chronic Heart Failure
Bibliographic record
Abstract
AIMS: Left bundle branch block (LBBB) increases morbidity and mortality in heart failure (HF). Heart rate reduction with ivabradine improves outcomes in patients with systolic HF. Therefore, we aimed to analyse the impact of LBBB on outcomes in patients with systolic HF as a function of heart rate, and the relationship between LBBB and the effect of treatment with ivabradine. METHODS AND RESULTS: Patients from the SHIFT (n = 6505) were divided into groups with (n = 912) or without (n = 5593) LBBB at baseline, and according to tertiles of heart rate (70-73, 74-80, and ≥81 b.p.m.). The effect of LBBB, heart rate, and ivabradine on the primary endpoint (cardiovascular death or HF hospitalization) and other endpoints was analysed. LBBB was associated with increases in the primary endpoint by 65%, cardiovascular mortality by 49%, HF hospitalization by 86%, and all-cause mortality by 49% (all P < 0.001). No interaction appeared between the impact of heart rate on outcomes and presence of LBBB (P = 0.83 for the primary endpoint); thus LBBB increases risk for all heart rates. No interaction was apparent in the effect of ivabradine with LBBB or without LBBB. Ivabradine did not increase the prevalence of bradycardia in patients with LBBB. CONCLUSION: LBBB increases risk in HF patients with heart rates ≥70 b.p.m. in sinus rhythm, unmodulated by heart rate. Ivabradine was safe in LBBB. Its effect was directionally similar to that in patients without LBBB, but did not reach statistical significance, possibly due to lack of power to test this effect because of the small number of LBBB patients.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".