Abstract 4147314: Contemporary Diagnosis, Management, and Outcomes of Patients With Low-Gradient Severe Aortic Stenosis: A Multi-Center Analysis
Bibliographic record
Abstract
Background: The American College of Cardiology/American Heart Association guidelines recommend assessing aortic stenosis (AS) using American Society of Echocardiography-endorsed parameters and referring patients with severe symptomatic AS for treatment. Yet, multi-site real-world assessment of guideline adherence is lacking. Methods: We assessed consecutive echocardiographic reports for patients >18 years of age from 30 US institutions with appropriate permissions between January 2018–March 2024 (egnite Database; egnite, Inc.). Completeness of echocardiographic evaluation of AS was assessed. Patients with severe AS were stratified into high- and low-gradient (HG, mean aortic gradient [MG] ≥40 mm Hg; LG, MG <40 mm Hg). Rates of Heart Team evaluation within 60 days and aortic valve replacement (AVR) within 6 months were assessed via the Kaplan-Meier method. Results: Of 2,829,095 echocardiographic reports, V max , MG, aortic valve area (AVA), stroke volume index, left ventricular ejection fraction (LVEF), and AS severity were missing in 25%, 30%, 35%, 83%, 5%, and 36%, respectively. Of 1,189,382 patients with available AS severity assessment, 45,967 had an AVA ≤1.0 cm 2 and/or V max ≥4.0 m/s with only 52% being diagnosed with severe AS and 20% being diagnosed as Conclusions: Key parameters needed to diagnose the severity of AS are often missing in echocardiographic reports. Compared to HG, severe LG AS patients have lower Heart Team evaluation rates, with racial, sex, and age disparities, as well as lower AVR rates and more observed mortality events. Further work is needed to enhance guideline adherence for the diagnosis and management of patients with severe LG 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".