Left Atrial Volume Assessed by Coronary Computed Tomography in Mid Ventricular Diastasis Predicts Adverse Events
Bibliographic record
Abstract
PURPOSE: Previous studies have demonstrated that left atrial (LA) volume has incremental prognostic value in predicting major adverse cardiac events (MACE). However, the predictive ability of LA volume in mid diastasis has not been investigated. We determined the incremental predictive value of LA volume indexed to body surface area (LAVi) measured in mid ventricular diastasis. MATERIALS AND METHODS: A total of 96 patients with MACE (all-cause mortality and nonfatal myocardial infarction) were matched to 96 controls without adverse events on follow-up. Coronary computed tomographic angiography images were reconstructed at the 75% phase (mid ventricular diastasis). LA volumes were measured and indexed to the body surface area. The predictive value of LAVi was assessed using Cox proportional hazard models for the MACE. RESULTS: LAVi was significantly larger (P<0.001) in the cases with adverse clinical outcomes (63.8±2.1 mL/m) versus the controls (50.3±1.2 mL/m). In a multivariate analysis, both significant coronary artery disease (defined as >70% stenosis in at least 1 coronary artery) and LAVi emerged as significant predictors of MACE with P-values of 0.0022 and 0.0001, respectively. CONCLUSIONS: A significantly larger LAVi was associated with MACE. LAVi was an incremental predictor to traditional clinical variables for MACE. The assessment of LAVi may be considered during coronary computed tomographic angiography and could potentially be incorporated into risk stratification and decision-making strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".