The Three-Dimensional Speckle Tracking Echocardiography in Distinguishing Between Ischaemic and Non-Ischaemic Aetiology of Heart Failure
Bibliographic record
Abstract
AIMS: The aim of this pilot study was to compare selected three-dimensional speckle tracking echocardiography (3D STE) parameters in patients with ischaemic and non-ischaemic aetiology of heart failure (HF) and to identify indices that can differentiate the two pathologies. METHODS AND RESULTS: Forty patients with left ventricular ejection fraction (LVEF) ≤ 40% were included to the study: 20 patients (age 63 ± 9.0 years, LVEF 29.0 ± 11.3%) with ischaemic cardiomyopathy and 20 patients (age 64.0 ± 11.0 years, LVEF 27.3 ± 7.5%) with non-ischaemic cardiomyopathy. All patients underwent two-dimensional (2D) and three-dimensional (3D) transthoracic echocardiography. Standard echocardiographic parameters, global longitudinal strain, and rotational parameters of left ventricle (LV) were assessed using 3D speckle tracking (3D STE). There were no differences in standard and STE parameters between the two groups. Among rotational parameters, the LV apical rotation (4.9 ± 3.5° vs. 2.3 ± 2.4°, P = 0.0022) was significantly higher in patients with ischaemic HF. Among all echocardiographic parameters, a cut-off value of 3.28° (area under the curve 0.78; 95% confidence interval, 0.62 to 0.93) was able to distinguish the ischaemic and non-ischaemic aetiology of HF with a sensitivity of 80% and specificity of 75%. CONCLUSIONS: This is the first study that compares 3D STE parameters between patients with ischaemic and non-ischaemic cardiomyopathy. It was proved that the apical rotation was significantly higher in patients with ischaemic cardiomyopathy. Our findings suggest that 3D STE might be useful in non-invasive differentiation between ischaemic and non-ischaemic aetiology of HF.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".