Risk Stratification of Patients With Moderate Aortic Stenosis Using Aortic Valve Calcium
Bibliographic record
Abstract
Abstract Objectives To evaluate whether aortic valve calcium (AVC) scoring by cardiac CT can enhance risk stratification and identify high-risk patients among those with moderate aortic stenosis (AS). Background Moderate AS is traditionally considered benign, yet emerging evidence suggests a subset may experience adverse outcomes similar to those with severe AS. AVC scoring has been validated for diagnosing severe AS but its prognostic value in moderate AS remains unclear. Methods A retrospective cohort study of 606 patients with moderate aortic stenosis (MAS) for whom aortic valve calcium (AVC) assessment was recommended on echocardiogram reports. Patients were categorized into two groups: those who underwent AVC (n=159) and those who did not (n=447). Primary outcomes included aortic valve replacement (AVR) and all-cause mortality. Secondary outcomes included time to severe AS diagnosis and time to AVR. Results A significantly higher proportion of patients in the CT cohort reached a diagnosis of severe AS (28.8% vs. 19.9%, p=0.037) and underwent valve intervention (32.6% vs. 14.1%, p<0.001) compared to the no-CT cohort. Patients underwent valve intervention mostly at the severe AS stage. All-cause mortality was markedly lower in the CT cohort (7.6% vs. 24.9%, p<0.001); difference being driven by excess non-cardiovascular mortality in the no-CT cohort (61.0% vs. 7.9%, p<0.001). Patients with severe AVC (males AVC >2000 AU and females >1200 AU) were more likely to undergo valve intervention compared with non-severe AVC (HR 2.60, 95% CI 1.42-4.74, p=0.002). Patients in the CT cohort were more likely to undergo valve intervention (HR 3.17, CI 2.04-4.91, p<0.001), and had a significantly lower all-cause mortality (HR 0.32, CI 0.16-0.61, p=0.001) compared to the no-CT cohort. Conclusion A practice algorithm incorporating AVC for risk stratification of MAS patients can lead to better outcomes. Role of AVC based decision-making in patients with MAS needs further study in a randomized trial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".