Accuracy of stroke volume measurement with phase-contrast cardiovascular magnetic resonance in patients with aortic stenosis
Bibliographic record
Abstract
BACKGROUND: Phase contrast (PC) cardiovascular magnetic resonance (CMR) in the ascending aorta (AAo) is widely used to calculate left ventricular (LV) stroke volume (SV). The accuracy of PC CMR may be altered by turbulent flow. Measurement of SV at another site is suggested in the presence of aortic stenosis, but very few data validates the accuracy or inaccuracy of PC in that setting. Our objective is to compare flow measurements obtained in the AAo and LV outflow tract (LVOT) in patients with aortic stenosis. METHODS: Retrospective analysis of patients with aortic stenosis who had CMR and echocardiography. Patients with mitral regurgitation were excluded. PC in the AAo and LVOT were acquired to derive SV. LV SV from end-systolic and end-diastolic tracings was used as the reference measure. A difference ≥ 10% between the volumetric method and PC derived SVs was considered discordant. Metrics of turbulence and jet eccentricity were assessed to explore the predictors of discordant measurements. RESULTS: We included 88 patients, 41% with bicuspid aortic valve. LVOT SV was concordant with the volumetric method in 79 (90%) patients vs 52 (59%) patients for AAo SV (p = 0.015). In multivariate analysis, aortic stenosis flow jet angle was a strong predictor of discordant measurement in the AAo (p = 0.003). Mathematical correction for the jet angle improved the concordance from 59 to 91%. Concordance was comparable in patients with bicuspid and trileaflet valves (57% and 62% concordance respectively; p = 0.11). Accuracy of SV measured in the LVOT was not influenced by jet eccentricity. For aortic regurgitation quantification, PC in the AAo had better correlation to volumetric assessments than LVOT PC. CONCLUSION: LVOT PC SV in patients with aortic stenosis and eccentric jet might be more accurate compared to the AAo SV. Mathematical correction for the jet angle in the AAo might be another alternative to improve accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".