Stress Echocardiography in Aortic Stenosis: From Diagnostic Challenges to Guideline-Endorsed Clinical Applications
Bibliographic record
Abstract
Aortic stenosis (AS) is the most common valvular heart disease in industrialized countries. Stress echocardiography (SE), using either exercise or dobutamine protocols, has emerged as a critical tool to overcome limitations of resting echocardiography, refine risk stratification, and guide the timing of aortic valve replacement. This review synthesizes contemporary evidence on the diagnostic, prognostic, and therapeutic role of SE in AS. Studies from all main databases (2000-2025) were systematically analyzed including prospective studies, consensus statements, and international guidelines. We highlight the physiological rationale, key prognostic markers, applications in asymptomatic severe and low-flow, low-gradient AS, and integration with multimodality imaging. SE is now guideline-endorsed for risk stratification in asymptomatic severe AS and the diagnosis of true severe versus pseudo-severe AS in low-flow, low-gradient disease. Future directions include advanced strain imaging, artificial intelligence, and broader adoption in the transcatheter era.
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 | 0.020 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".