Three-dimensional left atrial strain from retrospective gated computed tomography: Comparison with speckle-tracking echocardiography in patients with aortic stenosis
Bibliographic record
Abstract
Background Three-dimensional (3D) left atrial (LA) deformation assessment beyond the two-dimensional (2D) apical views circumvents atrial foreshortening and can be quantified from four-dimensional (4D) retrospective gated computed tomography (CT) using novel feature tracking methods. However, the consistency between CT-derived 3D and echocardiographic 2D peak left atrial longitudinal strain (PALS) has not been reported. We aimed to compare CT-derived 3D and echocardiographic 2D PALS in patients undergoing transcatheter aortic valve implantation (TAVI). Methods Eighty patients (81.8 ± 5.8 years, 30 % female) who underwent CT and transthoracic echocardiography (TTE) before TAVI were included. CT images were reconstructed at 5 % increments over the R–R interval. 4D CT-derived deformation was evaluated using novel feature tracking, from which 2D and 3D PALS CT were measured and compared with 2D PALS TTE . Results 2D PALS TTE and 2D PALS CT measurements were strongly correlated (Pearson coefficient = 0.84) and comparable (mean bias = −0.8; 95 % confidence interval, CI: -1.9 to 0.3), whereas 2D PALS TTE exhibited systematic overestimation compared with 3D PALS CT (mean bias = −3.6; 95 % CI: -4.7 to −2.5; P < 0.001). 3D PALS CT and 2D PALS TTE exhibited comparable correlations with exercise capacity and quality of life scores and prediction of elevated brain natriuretic peptide (area under the curve, AUC: 0.84, 0.79, respectively) and diastolic filling pressures (AUC: 0.65, 0.69, respectively). Conclusion 2D PALS CT and 2D PALS TTE showed good agreement, whereas 2D PALS TTE was systemically larger than 3D PALS CT which may be attributed to atrial foreshortening. 3D PALS CT circumvents limitations of 2D echocardiography and may offer adjunctive evaluation of atrial reservoir function in patients with valvular heart disease.
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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 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".