Echocardiographic predictors of outcomes in adults with aortic stenosis
Bibliographic record
Abstract
OBJECTIVE: The study purpose was to assess the usefulness of echocardiographic parameters of aortic stenosis (AS) severity and left ventricular (LV) systolic function to predict mortality in AS. The main hypothesis is that parameters of LV systolic function are the most important independent predictors of mortality, whereas parameters of stenosis severity are not. METHODS: 1065 consecutive patients with AS referred to the echocardiography laboratory and meeting the inclusion/exclusion criteria were included and followed during 5.7 years. The end points were aortic valve replacement (AVR) (n=584), composite of AVR or death (n=932), all-cause mortality (n=550) and cardiovascular mortality (n=398). RESULTS: The most powerful echocardiographic predictors of valve-related events were parameters of AS severity, such as peak aortic jet velocity (VPeak), mean gradient (MG) and aortic valve area (AVA) (all p<0.001). Regarding mortality, the main predictors were LV ejection fraction (LVEF) and stroke volume index (SVi) (p<0.05). After multivariable adjustment, LVEF (p<0.001) and SVi (p=0.02) remained the only echocardiographic predictors of mortality, even after adjustment for symptomatic status. AVA was also associated with mortality, whereas VPeak and MG were not. CONCLUSIONS: The most powerful echocardiographic predictors of mortality are low LVEF and low flow, whereas AS severity parameters predict valve-related events but not overall mortality. Hence, low flow should be integrated in the risk stratification and therapeutic decision-making in patients with AS.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".