Validation of the Valve Academic Research Consortium High Bleeding Risk Definition in Patients Undergoing TAVR
Bibliographic record
Abstract
BACKGROUND: The Valve Academic Research Consortium for High Bleeding Risk (VARC-HBR) has recently introduced a consensus document that outlines risk factors to identify high bleeding risk in patients undergoing transcatheter aortic valve replacement. The objective of the present study was to evaluate the prevalence and predictive value of the VARC-HBR definition in a contemporary, large-scale transcatheter aortic valve replacement population. METHODS: Multicenter study including 10 449 patients undergoing transcatheter aortic valve replacement. Based on consensus, 21 clinical and laboratory criteria were identified and classified as major or minor. Patients were stratified as at low, moderate, high, and very high bleeding risk according to the VARC-HBR definition. The primary end point was the rate of Bleeding Academic Research Consortium type 3 or 5 bleeding at 1 year, defined as the composite of periprocedural (within 30 days) or late (after 30 days) bleeding. RESULTS: Patients with at least 1 VARC-HBR criterion (n=9267, 88.7%) had a higher risk of Bleeding Academic Research Consortium 3 or 5 bleeding, proportional to the severity of risk assessment (10.8%, 16.1%, and 24.6% for moderate, high, and very-high-risk groups, respectively). However, a comparable rate of bleeding events was observed in the low-risk and moderate-risk groups. The area under receiver operating characteristic curve was 0.58. Patients with VARC-HBR criteria also exhibited a gradual increase in 1-year all-cause mortality, with an up to 2-fold increased mortality risk for high and very-high-risk groups (hazard ratio, 1.33 [95% CI, 1.04-1.70] and 1.97 [95% CI, 1.53-2.53], respectively). CONCLUSIONS: The VARC-HBR consensus offered a pragmatic approach to guide bleeding risk stratification in transcatheter aortic valve replacement. The results of the present study would support the predictive validity of the new definition and promote its application in clinical practice to minimize bleeding risk and improve patient outcomes.
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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".