Dobutamine stress echocardiography in low-gradient aortic stenosis
Bibliographic record
Abstract
Abstract Background Dobutamine stress echocardiography (DSE) is recommended to distinguish between true-severe and pseudo-severe aortic stenosis (AS) in patients with low flow low-gradients and left ventricular ejection fraction (LVEF) <50%. However, DSE has mostly been tested in the setting of LVEF<35%. Purpose The purpose of this study was to examine the diagnostic accuracy of DSE according to aortic valve calcification (AVC) score, in patents with low-flow, low-gradient AS across a wide range of LVEF. Methods Patients with aortic mean gradient <40 mmHg, aortic valve area (AVA) <1.0 cm2, and stroke volume index (SVi) ≤35 mL/m2 undergoing DSE were identified from three prospectively collected patient cohorts, and stratified according to LVEF; <35%, 35-50% and >50%. Severe AS was defined as AVC score ≥2000 AU among men, and ≥1200 AU for women. Recommendation for severe AS according to DSE, are an AVA <1.0 cm2 combined with an aortic mean gradient ≥40 mmHg or peak velocity ≥4 m/s. Receiver operating characteristic curves for DSE-derived AVA, mean gradient and Vmax were used to identify optimal cut-off points for each LVEF subgroup. Results Of the 221 patients included in the study, 78 (35%) presented with LVEF <35%, 67 (30%) with LVEF 35-50%, and 76 (34%) with LVEF >50%. Baseline characteristics are presented in the table. Based on AVC, severe AS was present in 102 (46%) of patients. Using recommended thresholds for severe AS for AVC and DSE, led to a sensitivity of 49%, a specificity of 84% and an accuracy of 67% for DSE for all patients with LVEF <50%, but with significant heterogeneity between groups figure. AVA performed rather uniformly across LVEF subgroups; (AUC =0.68 vs 0.62 vs 0.54, p=0.36) LVEF<35%, LVEF 35-50%, LVEF>50% respectively and displayed a rather uniform optimal cut-off (1.0 cm2 vs 0.9 cm2 vs 0.8 cm2). However, mean gradient and Vmax during DSE, both showed significantly diagnostic heterogeneity across LVEF groups (AUC= 0.90 vs 0.67 vs 0.65, p=<0.001 and AUC= 0.90 vs 0.66 vs 0.60, p=<0.001) with different optimal thresholds (30 mmHg & 3.8 m/s, 45 mmHg & 4.3 m/s and 37mmHg & 4 m/s, LVEF<35%, LVEF 35-50%, LVEF>50%, respectively). Conclusion Our study shows, that the association between DSE gradients and AS severity assessed by AVC demonstrates important heterogeneity depending on LVEF. While DSE had the highest accuracy in patients with LVEF<35% both patients with LVEF 35-50%, and patients with LVEF>50% showed significantly less concordance between DSE and AVC.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".