Abstract 16664: Aortic Valve Calcification is Prognostic for Mild Cognitive Impairment
Bibliographic record
Abstract
Introduction: While an association between vascular disease and dementia has been identified, few studies have assessed the longitudinal relationship between aortic valve calcification (AVC) and mild cognitive impairment (MCI). We recently found AVC to be associated with increased atherosclerotic events, and we sought to determine the prognostic value of AVC derived from low dose, lung cancer screening computed tomography (LCSCT) for MCI in a moderate-to-high atherosclerotic risk cohort. Methods: This was a single site, retrospective analysis of 1401 U.S. veterans (65 years [IQI: 61, 68] years; 97% male), who underwent quantification of AVC from LCSCT indicated for smoking history. Exclusion criteria included lung cancer, prior aortic valve replacement and prior MCI diagnosis. The primary outcome was new diagnosis of MCI identified by objective testing (Mini-Mental Status Exam or Montreal Cognitive Assessment) or by ICD coding. Secondary outcome was nonfatal cerebrovascular accident (CVA). Time-to-event analysis was carried out using AVC as a continuous and a categorical variable, and multivariate adjustment included age, diabetes mellitus, glomerular filtration rate <60 mL/min, coronary artery disease, and prior CVA. Results: Over a 5-year follow up, 110 patients (8%) were newly diagnosed with MCI and 45 patients (3%) had CVA. By Cox regression, AVC was predictive of MCI (HR: 1.15 [1.07 -1.24], p<0.001) and the association remained significant after multivariate adjustment (HR: 1.09 [1.01-1.18], p=0.026). Non-zero AVC tertiles were: 0.1-115; 116-427; and ≥428 Agatston Units. AVC was associated with MCI at increasing tertiles, and after multivariate analysis, the association remained significant (HR: 1.89 [1.09-3.28], p=0.024 and HR: 1.80 [1.01-3.20], p=0.047; tertiles 2 and 3, respectively). AVC was also associated with CVA (HR: 1.17 [1.05-1.32], p=0.006); however, the association lost significance after multivariate adjustment (HR: 1.12 [0.99-1.26], p=0.080). Conclusions: To our knowledge, this is the first study demonstrating that quantification of AVC from LCSCT is predictive of MCI. The association may be in part due to atherosclerotic thromboembolic events as there was a trend toward increasing nonfatal CVA in this population.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".