Evolution and prognostic impact of low flow after transcatheter aortic valve replacement
Bibliographic record
Abstract
OBJECTIVE: Low flow (LF), defined as stroke volume index (SVi) <35 mL/m(2), prior to the procedure has been recently identified as a powerful independent predictor of early and late mortality in patients undergoing transcatheter aortic valve replacement (TAVR). The objectives of this study were to determine the evolution of SVi following TAVR and to assess the determinants and impact on mortality of early postprocedural SVi (EP-SVi). METHODS: We retrospectively analysed the clinical, Doppler echocardiographic and outcome data prospectively collected in 255 patients who underwent TAVR. Echocardiograms were performed before (baseline), within 5 days after procedure (early post procedure) and 6 months to 1 year following TAVR (late post procedure). RESULTS: Patients with EP-SVi <35 mL/m(2) (n=138; 54%) had increased mortality (HR 1.97, p=0.003) compared with those with EP-SVi ≥35 mL/m(2) (n=117; 46%). Furthermore, patients with baseline SVi (B-SVi) <35 mL/m(2) and EP-SVI ≥35 mL/m(2), that is, normalised flow, had better survival (HR 0.46, p=0.03) than those with both B-SVi and EP-SVi <35 mL/m(2), that is, persistent LF, and similar survival compared with those with both B-SVi and EP-SVi ≥35 mL/m(2), that is, maintained normal flow. In a multivariable model analysis, EP-SVi was independently associated with increased risk of mortality (HR 1.41 per 10 mL/m(2) decrease, p=0.03). The preprocedural/intraprocedural factors associated with lower EP-SVi were lower B-SVi (standardised β [β] 0.36, p<0.001) atrial fibrillation (β -0.13, p=0.02) and transapical approach (β -0.22, p<0.001). CONCLUSIONS: The measurement of EP-SVi is useful to assess the immediate haemodynamic benefit of TAVR and to predict the risk of late mortality.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".