Value and limitations of peak-to-peak gradient for evaluation of aortic stenosis.
Bibliographic record
Abstract
BACKGROUND AND AIM OF THE STUDY: In patients with aortic stenosis (AS), it has been reported that the transvalvular pressure gradients (APs) may be reduced or even abolished in the presence of concomitant arterial hypertension, but the mechanisms underlying this phenomenon remain unclear. The study aim was to: (i) examine the relationship between systemic arterial hemodynamics and the peak-to-peak (deltaP(PtoP)), peak deltaP and mean deltaP; and (ii) propose and validate a new formula for the non-invasive estimation of the deltaP(PtoP) and of the peak left ventricular systolic pressure (LVSP) using Doppler echocardiography. METHODS: Two fixed stenoses (geometric orifice area 1.0 and 1.35 cm2) and one bioprosthesis (effective orifice area (EOA) 1.2 cm2) were tested in a mock flow circulation model. Systemic vascular resistance (R) was increased from 1,500 to 3,300 dyne.s/cm5, and systemic arterial compliance (C) was decreased from 2.9 to 0.9 ml/mmHg, while transvalvular flow was held constant. RESULTS: Neither C nor R had any significant impact on EOA, peak deltaP and mean deltaP. deltaP(PtoP) was decreased markedly, however, when C was reduced (bioprosthesis: -15 mmHg (-69%); orifice 1.35 cm2: -24 mmHg (-30%); cm2: (-13%)). Subsequently, an equation was proposed to predict deltaP(PtoP) from EOA, mean deltaP, and C measured by Doppler echocardiography. LVSP calculated by adding the predicted deltaP(Ptop) to systolic arterial pressure (SAP) was compared with LVSP measured directly in a dataset of 24 pigs with experimentally induced AS. There was a strong agreement between the estimated and measured LVSP (r = 0.97; mean absolute error 5 +/- 5 mmHg). CONCLUSION: deltaP(Ptop) should not be used to evaluate AS severity because, as opposed to peak and mean deltaPs, it is highly influenced by C. The new non-invasive method proposed in this study to estimate the LVSP may be useful for obtaining a more accurate estimate of global LV afterload in patients with 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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".