Outcomes and Safety of Transcaval Transcatheter Aortic Valve Replacement: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: The transcaval (TCv) vascular approach is increasingly used in transcatheter aortic valve replacement (TAVR) in patients unsuitable for the gold-standard transfemoral approach. We aimed to evaluate the efficacy, safety, and clinical outcomes associated with TCv-TAVR. METHODS: A systematic review and meta-analysis was conducted by searching PubMed/Medline, Embase, and the Cochrane Library for all articles assessing the TCv approach published through December 2023. Outcomes included 30-day and 1-year all-cause mortality (ACM), 30-day rehospitalisation, perioperative complications and postoperative complications at 30 days. The meta-analysis was registered on the PROSPERO database with the identifier CRD42024501921. RESULTS: A total of 8 studies with 467 patients were included. TCv-TAVR procedures achieved a success rate of 98.5%. TCv-TAVR was associated with a 30-day ACM rate of 6.1% (95% confidence interval [CI]: 3.9%-8.2%), a 1-year ACM rate of 14.9% (95% CI 2.3%-27.6%) and a 30-day rehospitalisation rate of 4.2% (95% CI -2.2% to 10.6%). Postoperative stroke or transient ischemic attack, major vascular complications, and major or life-threatening bleeding occurred in 3.3%, 8.7%, and 7.5% of cases, respectively. Cumulative meta-analyses showed a temporal trend of decreasing rates of vascular complications. CONCLUSIONS: The TCv approach in TAVR demonstrated a reassuring efficacy and safety profile, with mortality and postoperative complication rates similar to those reported for supra-aortic alternative TAVR access routes. The temporal decrease in vascular complications suggests potential improvements in procedural techniques and device technology. These findings further support the TCv approach as a viable option in patients ineligible for the transfemoral access. PROSPERO: CRD42024501921.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.036 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".