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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.000 | 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 teacher head, 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".