Trends and outcomes of mechanical circulatory support with transcatheter aortic valve replacement and transcatheter edge-to-edge repair of the mitral valve from the National Inpatient Sample, 2018 to 2021
Bibliographic record
Abstract
Background The use of mechanical circulatory support (MCS) devices with transcatheter aortic valve replacement (TAVR) and mitral transcatheter edge-to-edge repair (mTEER) is occasionally required; however, outcomes data are lacking.Methods We utilized the Nationwide Inpatient Sample database to identify hospital admissions of adults treated with TAVR and mTEER, with or without MCS, between 2018 and 2021.Results We identified 330,055 patients undergoing TAVR and mTEER, with 3240 in the MCS group and 326,815 in the non-MCS group. From 2018 to 2021, there was a steady increase in procedural volume (P for trend <0.001). Utilization of MCS remained stable (P for trend: total 0.096). The use of any MCS modality was associated with a >26-fold increase in mortality (1.01% vs 26.82%, P < 0.001). Mortality remained steadily high with MCS use (P for trend = 0.08). Length of stay and cost of hospitalization were higher in the MCS group (P < 0.05 for both).Conclusion The use of MCS in patients undergoing TAVR or mTEER was associated with higher mortality, morbidity, and healthcare utilization; however, causation cannot be determined given the inherent limitations of the dataset.
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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 | 0.001 | 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".