Relationship between Left and Right Ventricular Ejection Fractions in Chronic Advanced Systolic Heart Failure: Insights from the BEST Trial
Bibliographic record
Abstract
AIMS: Abnormally low right ventricular ejection fraction (RVEF) is a predictor of poor outcomes in chronic heart failure (HF) patients with low left ventricular ejection fraction (LVEF). However, little is known about the relationship between LVEF and RVEF in these patients. METHODS AND RESULTS: Of the 2707 Beta-blocker Evaluation of Survival Trial (BEST) participants with ambulatory chronic HF, New York Heart Association class III-IV symptoms, and LVEF ≤ 35%, 2008 patients had gated-equilibrium radionuclide angiographic data on baseline LVEF and RVEF. Patients were categorized into quartiles by LVEF ≥ 29% (n = 507), 23-28% (n = 513), 17-22% (n = 538), and < 17% (n = 450). Logistic regression models were used to determine the association of LVEF quartiles (reference, ≥ 29%) with abnormally low RVEF (<20%). The prevalence of RVEF < 20% for patients with LVEF quartiles ≥ 29, 23-28, 17-22, and < 17% were 3, 6, 15, and 32%, respectively. Unadjusted odds ratios [95% confidence intervals (CIs)] for RVEF < 20% (vs. ≥ 20%) associated with LVEF quartiles 23-28, 17-22, and < 17% (reference, ≥ 29%) were 2.18 (1.14-4.17; P = 0.018), 6.32 (3.54-11.30; P < 0.001), and 16.67 (9.46-29.39; P < 0.001), respectively. Respective multivariable-adjusted odds ratios (95% CIs) were 1.82 (0.94-3.54; P = 0.076), 4.55 (2.48-8.35; P < 0.001), and 10.53 (5.70-19.44; P< 0.001), respectively. Heart failure symptoms and signs had unadjusted associations with low RVEF, but lacked intrinsic associations. CONCLUSION: In patients with advanced systolic HF, LVEF has a strong dose-dependent relationship with RVEF which is independent of other characteristics, and low LVEF is useful as a surrogate marker of abnormally low RVEF in these patients.
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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 | 0.003 | 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 | 0.001 | 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".