Predicting response to cardiac resynchronization therapy: Use of strict left bundle branch block criteria
Bibliographic record
Abstract
BACKGROUND: Cardiac resynchronization therapy (CRT) reduces morbidity and mortality in heart failure with reduced ejection fraction (HFrEF). CRT efficacy is greater in left bundle branch block (LBBB). This study aimed to determine if strict LBBB criteria predict an improved QRS duration and left ventricular ejection fraction (LVEF) response after CRT. METHODS: HFrEF patients who received a CRT device at a single quaternary center were included. Patients were divided into three groups based on baseline QRS morphology. Group 1 consisted of patients with strict LBBB. Group 2 had conventional LBBB, and group 3 had non-LBBB morphology. Outcomes assessed included change in QRS duration after CRT, change in LVEF, and all-cause mortality. RESULTS: In 231 patients, 56% of patients were in group 1, 29% were in group 2, and 15% were in group 3. Patients with strict LBBB had a significant reduction in QRS duration (-20.9 ± 12.4 ms) compared to conventional LBBB (6.7 ± 19.4 ms; P < 0.0001) and non-LBBB (3.9 ± 29.3 ms; P < 0.0001). Patients with strict LBBB had a significant increase in LVEF (19.5 ± 10.2) compared to conventional LBBB (5.3 ± 12.6; P < 0.0001) and non-LBBB (-1.3 ± 10.9; P < 0.0001). There was moderate negative correlation between changes in QRS duration and LVEF (correlation coefficient = -0.63, P < 0.0001). Strict LBBB criteria were associated with a significant reduction in mortality compared to conventional LBBB (odds ratio 0.49, 95% confidence interval 0.24 to 0.99; P = 0.046). CONCLUSIONS: Strict LBBB predicted a reduction in QRS duration and an increase in LVEF compared to conventional LBBB and non-LBBB morphology in patients with HFrEF who received CRT.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".