Ejection fraction, B-lines, and global longitudinal strain evaluated with rest transthoracic echocardiography to assess prognosis in patients with chronic coronary syndromes
Bibliographic record
Abstract
Aim: Transthoracic echocardiography (TTE) is the first-line imaging test for patients with chronic coronary syndrome (CCS) and the cornerstone of risk stratification is left ventricular (LV) ejection fraction (EF). Aim of the study was to investigate the value of TTE supplemented with strain echocardiography (STE) and lung ultrasound (LUS) to assess the risk of patients with CCS. Methods: In a prospective, single-center, observational study, from November 2020 to December 2022, 529 consecutive patients with CCS were recruited. All patients were evaluated at rest. A single vendor machine (GE Vivid E95) was used. EF with biplane Simpson’s method (abnormal cut-off < 50%), LV global longitudinal strain (GLS%, abnormal cut-off ≤ 16.2% by receiver-operating characteristics analysis) by STE, and B-line score (abnormal cut-off ≥ 2) by LUS (4-site simplified scan) were assessed. Integrated TTE score ranged from 0 (all 3 parameters normal) to 3 (all parameters abnormal). All patients were followed-up and a composite endpoint was considered, including all-cause death, acute coronary syndrome (ACS), and myocardial revascularization. Results: During a follow-up of 14.2 months ± 8.3 months, 72 events occurred: 10 deaths, 11 ACSs, and 51 myocardial revascularizations. In multivariable analysis, B lines [hazard ratio (HR) 1.76, 95% confidence Interval (CI) 1.05–2.97; P = 0.03], and GLS ≤ 16.2% (HR 2.0, 95% CI 1.17–3.45; P = 0.01) were independent predictors of events. EF < 50% was a significant predictor in univariate, but not in multivariable analysis. Event rate at 2 years increased from score 0 (8%), to score 1 (21%), 2 (23%), and 3 (40%), P < 0.0001. Conclusions: TTE with left ventricular ejection fraction (LVEF) can be usefully integrated with STE for GLS, and LUS for B-lines, for better prediction of outcome in CCS. The 3 parameters can be obtained in every echo lab with basic technology, no harm, no risk, and no stress.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".