Percutaneous mechanical circulatory support from the collaborative multicenter Mechanical Unusual Support in <scp><b>T</b>AVI</scp> (<scp><b>MUST</b></scp>) Registry
Bibliographic record
Abstract
OBJECTIVES: To evaluate the use and outcomes of percutaneous mechanical circulatory support (pMCS) utilized during transcatheter aortic valve implantation (TAVI) from high-volume centers. METHODS AND RESULTS: Our international multicenter registry including 13 high-volume TAVI centers with 87 patients (76.5 ± 11.8 years, 63.2% men) who underwent TAVI for severe aortic stenosis and required pMCS (75.9% VA-ECMO, 19.5% Impella CP, 4.6% TandemHeart) during the procedure (prior to TAVI 39.1%, emergent rescue 50.6%, following TAVI 10.3%). The procedures were considered high-risk, with 50.6% having severe left ventricular dysfunction, 24.1% biventricular dysfunction, and 32.2% severe pulmonary hypertension. In-hospital and 1-year mortality were 27.5% and 49.4%, respectively. Patients with prophylactic hemodynamic support had lower periprocedural mortality compared to patients with rescue insertion of pMCS (log rank = 0.013) and patients who did not undergo cardiopulmonary resuscitation during the TAVI procedure had better short and long term survival (log rank <0.001 and 0.015, respectively). CONCLUSIONS: Given the overall survival rate and low frequency of pMCS-related complications, our study results support the use of pMCS prophylactically or during the course of TAVI (bailout) in order to improve clinical outcomes in high-risk procedures or in case of acute life-threatening hemodynamic collapse.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".