Usefulness of exercise-stress echocardiography for risk stratification of true asymptomatic patients with aortic valve stenosis
Bibliographic record
Abstract
Aims Abnormal exercise test defined as the occurrence of exercise limiting symptoms, fall in blood pressure below baseline, or complex ventricular arrhythmias is useful to predict clinical events in asymptomatic patients with aortic stenosis (AS). The purpose of this study was to determine whether exercise-stress echocardiography (ESE) adds any incremental prognostic value to resting echocardiography in patients with AS having a normal exercise response. Methods and results One hundred and eighty-six asymptomatic patients with at least moderate AS and preserved LV ejection fraction (>/=50%) were assessed by Doppler-echocardiography at rest and during a maximum ramp semi-supine bicycle exercise test. Fifty-one (27%) patients had an abnormal exercise test and were excluded from the present analysis. Among the 135 patients with normal exercise test, 67 had an event (aortic valve replacement motivated by symptoms or cardiovascular death) at a mean follow-up of 20 +/- 14 months. The variables independently associated with events were: age >/=65 years [hazard ratio (HR) = 1.96; 95% confidence interval (CI): 1.15-3.47; P = 0.01], diabetes, (HR = 3.20; 95% CI: 1.33-6.87; P = 0.01), LV hypertrophy (HR = 1.96; 95% CI: 1.17-3.27; P = 0.01), resting mean gradient >35 mmHg (HR = 3.60; 95% CI: 2.11-6.37; P < 0.0001), and exercise-induced increase in mean gradient >20 mmHg (HR = 3.83; 95% CI: 2.16-6.67; P < 0.0001). Conclusion The exercise-induced increase in transvalvular gradient may be helpful to improve risk stratification in asymptomatic AS patients with normal exercise response. These results thus suggest that ESE may provide additional prognostic information over that obtained from standard exercise testing and resting echocardiography.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".