Association Between the Presence of Coronary Artery Disease or Peripheral Artery Disease and Left Ventricular Mass in Patients Who Have Undergone Coronary Computed Tomography Angiography
Bibliographic record
Abstract
Background: Left ventricular mass (LVM) is a critical marker of future cardiovascular risk. We determined the association between LVM measured by coronary computed tomography angiography (CCTA) and the presence of coronary artery disease (CAD) or peripheral artery disease (PAD) in patients who had undergone CCTA for screening of CAD. Methods: We enrolled 1,307 consecutive patients (66 ± 12 years old, 49% males) who underwent CCTA for screening of CAD at the Fukuoka University Hospital (FU-CCTA registry), and either were clinically suspected of having CAD or had at least one cardiovascular risk factor. Patients with coronary stenosis of ≥ 50% by CCTA were diagnosed as CAD. Patients with an ankle brachial pressure index < 0.9 or who had already been diagnosed with PAD were considered to have PAD. Left ventricular mass index (LVMI), left ventricular ejection fraction (LVEF), end-diastolic volume (EDV) and end-systolic volume (ESV) were measured. The patients were divided into CAD (-) and CAD (+) or PAD (-) and PAD (+) groups. Results: The prevalences of CAD and PAD in all patients were 50% and 4.8%, respectively. Age, %males, %hypertension (HTN), %dyslipidemia (DL), %diabetes mellitus (DM), %smoking and %chronic kidney disease in the CAD (+) group were significantly higher than those in the CAD (-) group. Age, %males, %HTN, %DM and %smoking in the PAD (+) group were significantly higher than those in the PAD (-) group. CAD was independently associated with LVMI (odds ratio (OR): 1.01, 95% confidence interval (CI): 1.01 - 1.02, P < 0.01) in addition to age, male, HTN, DL, DM, and smoking. PAD was also independently associated with LVMI (OR: 1.01, 95% CI: 1.0 - 1.02, P = 0.018) in addition to age, DM, and smoking. Conclusions: LVMI determined by CCTA may be useful for predicting atherosclerotic cardiovascular diseases including both CAD and PAD, although there were considerable differences between %CAD and %PAD in all patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".