Hemodynamically-Guided Management of Heart Failure Across the Ejection Fraction Spectrum
Bibliographic record
Abstract
BACKGROUND: Hemodynamically-guided management using an implanted pulmonary artery pressure sensor is indicated to reduce heart failure (HF) hospitalizations in patients with New York Heart Association (NYHA) functional class II-III with a prior HF hospitalization or those with elevated natriuretic peptides. OBJECTIVES: The authors sought to evaluate the effect of left ventricular ejection fraction (EF) on treatment outcomes in the GUIDE-HF (Hemodynamic-GUIDEd management of Heart Failure) randomized trial. METHODS: The GUIDE-HF randomized arm included 1,000 NYHA functional class II-IV patients (with HF hospitalization within the prior 12 months or elevated natriuretic peptides adjusted for EF and body mass index) implanted with a pulmonary artery pressure sensor, randomized 1:1 to a hemodynamically-guided management group (treatment) or a control group (control). The primary endpoint was the composite of HF hospitalizations, urgent HF visits, and all-cause mortality at 12 months. The authors assessed outcomes by EF in guideline-defined subgroups ≤40%, 41%-49%, and ≥50%, within the trial specified pre-COVID-19 period cohort. RESULTS: There were 177 primary events (0.553/patient-year) in the treatment group and 224 events (0.682/patient-year) in the control group (HR: 0.81 [95% CI: 0.66-1.00]; P = 0.049); HF hospitalization was lower in the treatment vs control group (HR: 0.72 [95% CI: 0.57-0.92]; P = 0.0072). Within each EF subgroup, primary endpoint and HF hospitalization rates were lower in the treatment group (HR <1.0 across the EF spectrum). Event rate reduction by EF in the treatment groups was correlated with reduction in pulmonary artery pressures and medication changes. CONCLUSIONS: Hemodynamically-guided HF management decreases HF-related endpoints across the EF spectrum in an expanded patient population of patients with HF. (Hemodynamic-GUIDEd Management of Heart Failure [GUIDE-HF]; NCT03387813).
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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) | 0.001 | 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 | 0.001 | 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".