Incremental Value of Bi-Ventricular Ejection Fraction (BiVEF) Phenotyping for the Discrimination of Heart Failure Symptoms and Clinical Outcomes: A Cardiovascular Magnetic Resonance Study of 9,437 Patients
Bibliographic record
Abstract
ABSTRACT Background Left ventricular ejection fraction (LVEF) continues to be employed as the principle phenotypic marker for the classification, prognostication, and management of cardiovascular disease. However, expanding evidence identifies similarly important roles for right ventricular ejection fraction (RVEF) across multiple referral cohorts, raising the consideration of bi-ventricular ejection fraction (BiVEF) based phenotyping in broader clinical practice. We assessed the value of cardiovascular magnetic resonance (CMR)-based BiVEF phenotyping versus conventional LVEF-only phenotyping for the prediction of NYHA functional class and future heart failure (HF) outcomes. Methods 9,437 consecutively enrolled adult patients clinically referred for CMR were evaluated for NYHA class ≥II at time of imaging and a future composite outcome of HF hospitalization, HF death, and need for cardiac transplantation or LV assist device. Results Median age was 57 years (Q1, Q3 44-66, 62% male). Across all LVEF strata, RVEF<45% was independently associated with NYHA ≥II after comprehensive adjustment for baseline clinical and imaging characteristics. Respective adjusted odds ratios for RVEF <45% versus ≥45% were 2.30 (1.79-2.97), 1.58 (1.13-2.20), and 2.01 (1.44-2.79) for LVEF <40%, 40-50, and >50% categories (p<0.001, =0.007, and <0.001; respectively). Over a median follow-up of 3.9 years, 766 patients (8%) experienced the HF outcome. In a multivariable Fine-Gray model, the respective adjusted HR sub for LVEF <40% and 40-50% were 2.13 (1.64-2.77) and 1.70 (1.33-2.17); p<0.001) relative to LVEF >50%. In this model, RVEF <45% was associated with 1.52 (1.25-1.86) greater hazard for future HF outcome versus RVEF ≥45% (p<0.001). Conclusions RV contractile health is independently associated with HF symptoms and identifies patients at elevated risk of future HF outcomes. Incremental prognostic value from BiVEF phenotyping is delivered versus LVEF-only phenotyping.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".