Abstract 10115: The Diagnostic Value of Global and Territorial Longitudinal Strain at Rest for the Detection of Coronary Artery Disease in Patients Without Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Background: Global longitudinal strain (GLS) at rest aids the detection of coronary artery disease (CAD). However, myocardial strains are affected by both ischemia and diabetes mellitus (DM), and previous studies that evaluated the performance of GLS for detecting ischemia always included a certain DM patients in the study population. Thus, we sought to investigate in patients with no DM the power of GLS for detecting three-vessel CAD, and whether territorial longitudinal strain (TLS) could help identifying individual coronary artery stenosis Methods and results: We retrospectively studied 211 consecutive patients with suspected CAD and normal left ventricular (LV) ejection fraction. The patients with DM were excluded. All patients underwent echocardiography and subsequently coronary angiography. LV global and segmental peak systolic longitudinal strain (PSLS) parameters were quantified by two-dimensional speckle tracking echocardiography (2D STE). Territorial PSLSs were calculated based on the perfusion territories of the 3-epicardial coronary arteries in a 17-segment LV model. Critical CAD was defined as luminal diameter stenosis ≥ 70% in ≥ 1 epicardial coronary artery. Totally 145 patients had critical CAD on coronary angiography. Significant differences were observed in all strain parameters between patients with and without CAD. The AUC for GLS in the detection of three-vessel CAD was 0.875 at a cutoff value of -19.05% with sensitivity 78.1% and specificity 72.7%, which increased to 0.926 after excluding apical segments (cutoff value -18.66%; sensitivity 84.4% and specificity 81.8%). The TLS values were significantly lower in regions supplied by stenotic compared with non-stenotic coronary arteries. It has better power to identify LCX and LAD stenosis than RCA stenosis. An area under the curve (AUC) for the TLS to identify critical LCX, LAD and RCA stenosis, in order of diagnostic accuracy, is 0.818 for LCX, 0.764 for LAD and lastly 0.723 for RCA. Conclusions: In patients with no DM and suspected CAD, GLS is an excellent predictor of three-vessel CAD with high accuracy. A higher cut point than that reported before was obtained and should be used. TLS could identify which coronary artery is stenotic with fair sensitivity and specificity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".