Prognostic value of systolic left ventricular ejection fraction using prospective ECG-triggered cardiac CT
Bibliographic record
Abstract
Background Prospective ECG-triggered cardiac computed tomography (CT) imaging limits the ability to assess left ventricular (LV) ejection fraction (EF). We previously developed a new index derived from LV volume changes over 100 ms during systole (LVEF 100msec ) as a surrogate of LV function in patients undergoing prospective ECG-triggered cardiac CT. We sought to evaluate the prognostic value of LVEF 100msec . Methods Patients undergoing prospective systolic ECG-triggered cardiac CT were enrolled between January 2015 and September 2022. Each CT was analyzed for LVEF 100msec . Area under the curve analysis and Cox proportional hazards models were used to define the best LVEF 100msec cut-off and to predict major adverse cardiovascular events (MACE), defined as a composite of all-cause death, cardiac death/arrest, non-fatal myocardial infarction, and stroke. Results The study enrolled 313 patients (median age = 58 years, male = 52.4 %). During a median follow-up of 924 (660–1365) days, 24 (7.7 %) patients had MACE. LVEF 100msec was significantly lower in the MACE group compared to the non-MACE group (4.8 % vs. 8.3 %, p = 0.002). Optimal LVEF 100msec cut-off for predicting MACE was 6.3 %. MACE-free survival rate was significantly lower in patients with LVEF 100msec ≤6.3 % than those with >6.3 % (p < 0.001). LVEF 100msec ≤6.3 % was an independent predictor of MACE, with an adjusted hazard ratio of 3.758 (95 % CI, 1.543–9.148; p = 0.004). The prognostic value of LVEF 100msec was consistent across the various severities of coronary artery disease. Conclusion LVEF 100msec was an independent predictor of adverse events. The implementation of LVEF 100msec may improve the prognostic value of prospective ECG-triggered cardiac CT.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".