Left ventricular ejection fraction is associated with intraoperative circulatory collapse during transcatheter aortic valve implantation
Bibliographic record
Abstract
Background: Intraoperative hemodynamic collapse during transcatheter aortic valve implantation (TAVI) is a devastating complication that requires mechanical support. In this study, we sought to analyze our early experience in using cardiopulmonary bypass (CPB) support to circumvent circulatory compromise during TAVI. Methods: Between January 2018 and December 2020, 102 consecutive patients (54 males; mean age, 71.2±8.9 years) received TAVI at Tianjin Chest Hospital, and an emergency CPB device was used in 6 of these patients (5.9%). The clinical data of the CPB and no-CPB groups were analyzed to identify the factors associated with intraoperative hemodynamic collapse requiring CPB. Results: All 6 patients who needed emergency CPB support were successfully weaned from the device. This group had a higher Society of Thoracic Surgeons Score [4.09 (2.02, 6.85) vs. 7.47 (5.07, 23.46); P=0.030], more patients with a left ventricular ejection fraction (LVEF) ≤30% [4 (66.7%) vs. 2 (2.1%); P=0.000], a larger right ventricle anteroposterior diameter [20.50 (19.75, 21.25) vs.19.00 (17.00, 20.00); P=0.016], and a higher degree of aortic regurgitation [4.50 (2.75, 5.00) vs. 2.00 (1.00, 4.00); P=0.018] compared to the no-CPB group. The CPB group also had a higher in-hospital mortality rate than did the no-CPB group (16.7% vs. 4.7%; P=0.026). Multivariable analysis determined that the presence of lower pre-TAVI LVEF was associated with intraoperative hemodynamic collapse. Conclusions: Our results indicate that LVEF is an independent risk factor for requiring emergency CPB during the TAVI procedure. The need for emergency CPB support was associated with higher in-hospital mortality.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".