Systolic hypertension and progression of aortic valve calcification in patients with aortic stenosis: results from the PROGRESSA study
Bibliographic record
Abstract
Hypertension is highly prevalent in patients with aortic stenosis (AS) and is associated with worse outcomes. The current prospective study assessed the impact of systolic hypertension (SHPT) on the progression of aortic valve calcification (AVC) measured by multidetector computed tomography (MDCT) in patients with AS. The present analysis includes the first series of 101 patients with AS prospectively recruited in the PROGRESSA study. Patients underwent comprehensive Doppler echocardiography and MDCT exams at baseline and after 2-year follow-up. AVC and coronary artery calcification (CAC) were measured using the Agatston method. Patients with SHPT at baseline (i.e. systolic blood pressure ≥140 mmHg; n = 37, 37%) had faster 2-year AVC progression compared with those without SHPT (i.e. systolic blood pressure <140 mmHg) (AVC median [25th percentile–75th percentile]: +370 [126–824] vs. +157 [58–303] AU; P = 0.007, respectively). Similar results were obtained with the analysis of AVC progression divided by the cross-sectional area of the aortic annulus (AVCdensity: +96 [34–218] vs. +45 [14–82] AU/cm2, P = 0.01, respectively). In multivariable analysis, SHPT remained significantly associated with faster progression of AVC or AVCdensity (all P = 0.001). There was no significant difference between groups with respect to progression of CAC (+39 [3–199] vs. +41 [0–156] AU, P = 0.88). This prospective study shows for the first time that SHPT is associated with faster AVC progression but not with CAC progression in AS patients. These findings provide further support for the elaboration of randomized clinical trials to assess the efficacy of antihypertensive medication to slow the stenosis progression in patients with AS.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".