Usefulness of aortic valve calcification in patients with low flow aortic stenosis
Bibliographic record
Abstract
ABSTRACT Background Aortic valve calcification (AVC) has been shown to be a powerful assessment of aortic stenosis severity (AS) and predictor of adverse outcome. However, its accuracy in patients with low-flow AS has not yet been proven. Objectives To assess the predictive value of AVC in patients classical (CLF, i.e. low left ventricular ejection fraction [LVEF]) or paradoxical (PLF, i.e. low flow preserved LVEF) AS patients. Methods We prospectively include 641 patients, 319 (49.8%) with CLF-AS and 322 (50.2%) with PLF-AS who underwent Doppler-echocardiography and multidetector computed tomography. AVCratio was calculated as AVC divided by the sex-specific AVC threshold for AS-severity; AVC score ≥2,000 AU in males, and ≥1,200 AU in females. The primary endpoint of the study was all-cause mortality regardless of treatment. Results During a median follow-up of 4.9 (4.3-5.9) years there were 265 deaths. After comprehensive adjustment, AVCratio was associated with all-cause mortality in CLF-AS (aHR=1.25 [1.01-1.56]; p<0.05) and PLF-AS (aHR=1.51[1.14-2.00]; p=0.004) patients. There was an interaction (p=0.001) between AVC and AS flow pattern (i.e. CLF vs. PLF) with regard to the prediction of mortality. The best AVCratio threshold to predict mortality was different in CLF-AS (AVCratio≥0.7) and PLF-AS (AVCratio≥1) patients. After comprehensive analysis, AVCratio as a dichotomic variable was associated with all-cause mortality in all groups (p≤0.001). The addition of AVCratio to the models improved all model’s predictive value (all net reclassification index >18%; all p≤0.05). Conclusion In patients with CLF or PLF AS, AVC is a major predictor of mortality. Thus, AVC should be used in low flow patients to stratify risk. Importantly, in patients with reduced LVEF, a non-severe AS (i.e. AVC 70% of severe) could be associated with reduce survival. Clinical Perspective What is new? Aortic valve calcification is a powerful predictor of outcome in patients with low ejection fraction aortic stenosis and in patients with low-flow despite normal ejection fraction aortic stenosis. In patient with low ejection fraction aortic stenosis, a non-severe calcification (AVCratio=0.7) is associated with increased mortality. An AVCratio of 0.7 correspond to an AVC of 840AU in female patients and 1,400AU in male patients. What are the clinical implications? AVC should be used in low ejection fraction and low flow patients to assess aortic stenosis severity and stratify risk. A severe AVC, in patient with low-flow preserved ejection fraction, could help in clinical decision making. A moderate-to-severe AVC (i.e. AVCratio>0.7), in patients with low ejection fraction, is detrimental and may be used to refine clinical decision making.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".