H2FPEF and HFA-PEFF scores performance and the additional value of cardiac structure and function in patients with atrial fibrillation
Bibliographic record
Abstract
Background The H 2 FPEF and the HFA-PEFF scores have become useful tools to diagnose heart failure with preserved ejection fraction (HFpEF). Their accuracy in patients with a history of atrial fibrillation (AF) is less known. This study evaluates the association of these scores with invasive left atrial pressure (LAP) and the additional value of cardiac measures. Methods This is a multicenter observational prospective study involving patients undergoing ablation of AF. Patients with left ventricular ejection fraction (LVEF) < 40%, congenital cardiopathy, any severe cardiac valve disease and prosthetic valves were excluded. Elevated filling pressure was defined as a mean LAP ≥15 mmHg. Results A total of 135 patients were enrolled in the study (mean age 65.2 ± 9.1 years, 32% female, mean LVEF 56.9 ± 7.9%). Patients with H 2 FPEF ≥ 6 or HFA-PEFF ≥5 had higher values of NTproBNP and more impaired cardiac function. However, neither H 2 FPEF nor HFA-PEFF score showed a meaningful association with elevated mean LAP (respectively, OR 1.05 [95%CI 0.83–1.34] p = 0.64, and OR 1.09 [95%CI: 0.86–1.39] p = 0.45). The addition of LA indexed minimal volume (LAVi min) improved the ability of the scores (baseline C-statistic 0.51 [95%CI 0.41–0.61] for the H 2 FPEF score and 0.53 [95%CI 0.43–0.64] for the HFA-PEFF score) to diagnose elevated filling pressure (H 2 FPEF + LAVi min: C-statistic 0.70 [95%CI 0.60–0.80], p -value = 0.005; HFA-PEFF + LAVi min: C-statistic 0.70 [95%CI 0.60–0.80], p-value = 0.02). Conclusion In a cohort of patients with a history of AF, the use of the available diagnostic scores did not predict elevated mean LAP. The integration of LAVi min improved the ability to correctly identify elevated filling pressure.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".