Assessment of the American Society of Echocardiography-European Association of Echocardiography guidelines for diastolic function in patients with depressed ejection fraction: an echocardiographic and invasive haemodynamic study
Bibliographic record
Abstract
AIMS: There is controversy surrounding the accuracy of echo-Doppler variables, including early mitral inflow/mitral annular velocity (E/e'), for estimating left ventricular filling pressure (LVFP) in patients with depressed ejection fraction (EF < 50%). METHODS AND RESULTS: The American Society of Echocardiography-European Association of Echocardiography (ASE-EAE) algorithm for diastolic function in depressed LVEF was retrospectively applied to a database of patients who underwent echocardiography ≤20 min of cardiac catheterization. LV pre-atrial contraction pressure (pre-A) ≥15 mmHg was elevated. Of 62 patients studied, the mean age was 53.6 ± 10.6 years and the mean LVEF was 27.2 ± 11.8%. The correlations of E/e' (R = 0.43, P = 0.0005) and E (R = 0.39, P = 0.002) with LV pre-A were modest, compared with pulmonary artery pressure (PAP, R = 0.69, P = 0.0006), E/late mitral (A) velocity (R = 0.52, P < 0.0001), and mitral deceleration time (DT, R = -0.51, P < 0.0001). Using the ASE-ESE algorithm starting with E/A, E, and DT, 54 of 62 patients were accurately classified to predict LV pre-A >15 or <15 mmHg (sensitivity = 84%, specificity = 80%, area under the curve = 0.86, P < 0.001). The 6 of 6 patients with E/A < 1 and E < 50 and the 14 of 15 (93%) patients with E/A> 2 and DT < 150 were correctly classified as having normal and elevated LVFP, respectively, while 34 of 41 (83%) patients with E/A = 1-2 or E/A<1 and E>50 cm/s were correctly classified using the addition of E/e' and PAP. CONCLUSION: This retrospective study shows that in this population with depressed LVEF, no single echo-Doppler variable had high accuracy for predicting LV pre-A ≥15 mmHg. However, the ASE-EAE algorithm using multiple variables predicted LVFP with good accuracy, superior to any single echo-Doppler variable alone.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".