Impact of High Body Mass Index on Vascular and Bleeding Complications After Transcatheter Aortic Valve Implantation
Bibliographic record
Abstract
Increased body mass index (BMI) is an established cardiovascular risk factor. The impact of high BMI on vascular and bleeding complications in patients undergoing transcatheter aortic valve implantation (TAVI) is not clarified. RISPEVA, a multicenter prospective database of patients undergoing TAVI stratified by BMI was used for this analysis. Patients were classified as normal or high BMI (obese and overweight) according to the World Health Organization criteria. A comparison of 30-day vascular and bleeding outcomes between groups was performed using propensity scores methods. A total of 3776 matched subjects for their baseline characteristics were included. Compared with normal BMI, high BMI patients had significantly 30-day greater risk of the composite of vascular or bleeding complications (11.1% vs 8.8%, OR: 1.28, 95% CI [1.02 to 1.61]; p = 0.03). Complications rates were higher in both obese (11.3%) and overweight (10.5%), as compared with normal weight patients (8.8%). By a landmark event analysis, the effect of high versus normal BMI on these complications appeared more pronounced within 7 days after the TAVI procedure. A significant linear association between increased BMI and vascular complications was observed at this time frame (p = 0.03). In conclusion, compared with normal BMI, both obese and overweight patients undergoing TAVI, experience increased rates of 30-day vascular and bleeding complications. These findings indicate that high BMI is an independent risk predictor of vascular and bleeding complications after TAVI.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 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".