Clinical utility of systolic left ventricular ejection fraction in atrial fibrillation: Role of prospective ECG-triggered cardiac CT
Bibliographic record
Abstract
Background The assessment of left ventricular (LV) systolic function and quantification of LV ejection fraction (LVEF) in patients with atrial fibrillation (AF) can be difficult. We previously demonstrated that LV volume changes over the 100 ms of systole (LVEF 100ms ) can be used as a measure of LV systolic function. Objective We sought to evaluate the applicability of LVEF 100ms in patients with AF. Methods We screened patients with AF who underwent prospective systolic electrocardiogram-triggered cardiac computed tomography from January 2015 to June 2023. The correlation between LVEF 100ms and echocardiography-derived LVEF was assessed. Patients were categorized into 3 groups on the basis of echocardiographic LVEF (≤40%, 40%–55%, and ≥55%), and LVEF 100ms was compared among these groups. Receiver operating characteristic curve analysis and Cox proportional hazards models were used to determine the optimal LVEF 100ms cutoff for predicting LVEF ≤ 40% and major adverse cardiovascular events ( MACE ), defined as a composite of cardiac death, myocardial infarction, heart failure hospitalization, and stroke. Results Of the total 123 patients, 62 (50.4%) had an LVEF of ≥55%, 40 (32.5%) had an LVEF of 40%–50%, and 21 (17.1%) had an LVEF of ≤40%. LVEF 100ms correlated with echocardiography-derived LVEF ( P < .001) and differed significantly among groups ( P < .001). LVEF 100ms ≤ 3.3% predicted LVEF ≤ 40% (area under the curve 0.809; sensitivity 87%; specificity 67%). Patients with an LVEF 100ms of ≤3.3% had a higher rate of MACE than did those without ( P = .030), and LVEF 100ms ≤ 3.3% was an independent predictor of MACE. Conclusion LVEF 100ms can provide a useful indicator of LV dysfunction in patients with AF undergoing prospective electrocardiogram-triggered cardiac computed tomography.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".