Estimating Left Ventricular Filling Pressure by Echocardiography
Bibliographic record
Abstract
BACKGROUND: The diagnosis of heart failure may be challenging because symptoms are rather nonspecific. Elevated left ventricular (LV) filling pressure may be used to confirm the diagnosis, but cardiac catheterization is often not practical. Echocardiographic indexes are therefore used as markers of filling pressure. OBJECTIVES: This study investigated the feasibility and accuracy of comprehensive echocardiography in identifying patients with elevated LV filling pressure. METHODS: We conducted a multicenter study of 450 patients with a wide spectrum of cardiac diseases referred for cardiac catheterization. Left atrial volume index, in combination with flow velocities and tissue Doppler velocities, was used to estimate LV filling pressure. Invasively measured pressure was used as the gold standard. RESULTS: Mean left ventricular ejection fraction (LVEF) was 47%, with 209 patients having an LVEF <50%. Invasive measurements showed elevated LV filling pressure in 58% of patients. Clinical assessment had an accuracy of 72% in identifying patients with elevated filling pressure, whereas echocardiography had an accuracy of 87% (p < 0.001 vs. clinical assessment). The combination of clinical and echocardiographic assessment was incremental, with a net reclassification improvement of 1.5 versus clinical assessment (p < 0.001). CONCLUSIONS: Echocardiographic assessment of LV filling pressure is feasible and accurate. When combined with clinical data, it leads to a more accurate diagnosis, regardless of LVEF.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".