New Cardiovascular Damage Staging Classification for Chronic Significant Aortic Regurgitation
Bibliographic record
Abstract
BACKGROUND: Current recommendations for management of chronic aortic regurgitation (AR) are based on AR severity, symptoms and repercussions on the left ventricle (LV), only providing modest risk stratification value in patients with AR. The objective was to assess the prognostic value of a new staging classification characterizing the extent of cardiovascular damage for risk stratification in patients with chronic AR. METHODS: According to cardiovascular damages at echocardiography, 619 patients with ≥ moderate AR were classified as follows: Stage 0: no cardiovascular damage; Stage 1: LV damage; Stage 2: ascending aorta, left atrial, or mitral valve damage; Stage 3: pulmonary vasculature or tricuspid valve damage; and Stage 4: right ventricular damage. Study primary endpoint was all-cause mortality and the secondary endpoint was a composite of all-cause mortality and cardiovascular hospitalization. RESULTS: Among 619 patients, 85 (13.7%) were in Stage 0, 138 (22.3%) in Stage 1, 278 (44.9%) in Stage 2, 35 (5.7%) in Stage 3, and 83 (13.4%) in Stage 4. After adjustments, cardiovascular damage stage was independently associated with increased risk of all-cause mortality (hazard ratio [HR]; 95% confidence interval [CI] per 1 stage increase, 1.49 [1.27 to 1.75]; P < 0.001) and the composite of events (HR,1.17 [1.03-1.34]; P = 0.02). Stage ≥ 2 was independently associated with increased risk of all-cause mortality (HR, 3.02 [1.83-4.99]; P < 0.001) and the composite of events (HR, 1.88 [1.29-2.73]; P < 0.001). Net reclassification index analyses showed incremental value of the new classification (net reclassification index [NRI] = 0.40; P < 0.001). CONCLUSIONS: This new cardiovascular damage staging classification provides powerful prognostic value in patients with chronic AR and may be useful to enhance risk stratification and trigger intervention, particularly in asymptomatic AR.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".