Abstract 10881: Importance of Stroke Volume Index Assessed by Echocardiography for Stratification of Low Gradient Severe Aortic Stenosis With Normal Left Ventricular Function
Bibliographic record
Abstract
Background: The prognosis and need for aortic valve intervention in patients with low-gradient (LG) severe aortic stenosis (AS) and normal left ventricular (LV) function remain controversial. We hypothesized that echocardiography (echo) could be used for the stratification of high risk patients when accurate assessment of stroke volume (SV) is obtained. Methods: The study included 207 patients with LG severe AS (aortic valve area (AVA) ≤ 1.0cm2, mean gradient < 40mmHg) and normal LV function (LVEF ≥ 50%). In order to improve the accuracy of SV calculation by echo, we measured the LV outflow tract diameter (LVOT) at two locations: 5mm below the aortic annulus (recommended by the American Society of Echocardiography) and at the aortic valve annulus (common in clinical practice). Patients were divided into two groups based on SV index (SVi). Group I had SVi < 35mL/m2 (n=75) and Group II had SVi ≥ 35mL/m2 (n=97) calculated based on both locations using continuity equation. Patients with discrepancy in SVi calculation at the two LVOT locations (potential LVOT-velocity mismatch) were excluded (n=35). Mean follow-up time was 3.0±1.6 years. Results: There was no significant difference in age (78±11 vs. 77±11 years), LVEF (58±6 vs. 60±6%), mean aortic valve gradient (30±5 vs. 31±5 mmHg) and LV mass index (115±34 vs. 109±31 g/m2) between Group I and Group II. Group I had larger body surface area (1.97±0.25 vs. 1.84±0.25 m2), smaller AVA index (0.36±0.08 vs. 0.50±0.09 cm2/m2), higher valvuloarterial impedance (6.1±1.6 vs. 4.0±0.8 mmHg/mL/m2) and lower 3-year cumulative survival (61% vs. 75%, p=0.03) than Group II. Multivariable analysis showed SVi was a strong predictor of mortality among LG severe AS patients (HR 0.95, CI: 0.91-0.99, P=0.02). For Group I, those with medical management had a two-fold increased risks of death compared to those who underwent AS intervention (HR: 2.74, CI: 1.13-6.67, p=0.03). However, there was no survival benefit associated with AS intervention in Group II (HR: 0.73, CI: 0.29-1.80, p=0.49). Conclusion: LG severe AS with normal LV function consists of heterogeneous patients carrying different prognosis depending on SVi. If low SVi is accurately assessed by Doppler echo, AS intervention may be beneficial. Further clinical trials are warranted.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".