Abstract 9464: Left Ventricular Ejection Fraction Trajectory Predicts Outcomes in Patients With Heart Failure and Mildly Reduced Ejection Fraction
Bibliographic record
Abstract
Introduction: Heart failure and mildly reduced ejection fraction (HFmrEF) is associated with a favourable prognosis compared heart failure with reduced EF. However, HFmrEF may be a transitory state and serial imaging might clarify if these patients demonstrate an increasing, decreasing or stable EF. Hypothesis: LVEF trajectory can identify sub-groups of patients with HFmrEF with different prognosis. Materials and Methods: Patients with a diagnosis of HF and at least two echocardiograms performed ≥6 months apart were included if the LVEF measured 40-49% on the second study. They were classified based on change from their first echocardiogram as: a) HFmrEF-Incr if LVEF had increased ≥ 10% (n=450), b) HFmrEF-Dec if LVEF had decreased ≥10% (n=512), or c) HFmrEF-stable if they did not meet the other criteria (n=389). The primary outcome was a composite of all-cause mortality or cardiovascular hospitalization (ACM/CVH). Associations with time to first event were assessed with multivariable Cox analyses adjusted for age, medical history, and medications. Results: In total, 1351 patients with HFmrEF were included (median age 74, 35.8% women). During a median follow-up of 15.3 months, ACM/CVH occurred in 811 patients (324 ACM, 487 CVH). HFmrEF-Incr was associated with a lower incidence of ACM/CVH compared to patients with HFmrEF-Stable (adjusted HR 0.72, 95% CI 0.59 - 0.87, p<0.001). Patients with HFmrEF-Dec were more likely to experience the composite outcome in unadjusted analyses (unadjusted HR 1.19, p=0.040) but not adjusted analyses (adjusted HR 1.13, 95% CI 0.96 - 1.34, p=0.140). Conclusions: Patients with HFmrEF and positive trajectory of LVEF were less likely to experience adverse outcomes, while those with a negative LVEF trajectory demonstrated a trend to higher risk. Categories based on LVEF trajectory provide clinically meaningful information and may help physicians make decisions regarding the need to pursue more aggressive medical therapy and surveillance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".