A streamlined pathway for transcatheter aortic valve implantation: the BENCHMARK study
Bibliographic record
Abstract
BACKGROUND AND AIMS: There is significant potential to streamline the clinical pathway for patients undergoing transcatheter aortic valve implantation (TAVI). The purpose of this study was to evaluate the effect of implementing BENCHMARK best practices on the efficiency and safety of TAVI in 28 sites in 7 European countries. METHODS: This was a study of patients with severe symptomatic aortic stenosis (AS) undergoing TAVI with balloon-expandable valves before and after implementation of BENCHMARK best practices. Principal objectives were to reduce hospital length of stay (LoS) and duration of intensive care stay. Secondary objective was to document patient safety. RESULTS: Between January 2020 and March 2023, 897 patients were documented prior to and 1491 patients after the implementation of BENCHMARK practices. Patient characteristics were consistent with a known older TAVI population and only minor differences. Mean LoS was reduced from 7.7 ± 7.0 to 5.8 ± 5.6 days (median 6 vs. 4 days; P < .001). Duration of intensive care was reduced from 1.8 to 1.3 days (median 1.1 vs. 0.9 days; P < .001). Adoption of peri-procedure best practices led to increased use of local anaesthesia (96.1% vs. 84.3%; P < .001) and decreased procedure (median 47 vs. 60 min; P < .001) and intervention times (85 vs. 95 min; P < .001). Thirty-day patient safety did not appear to be compromised with no differences in all-cause mortality (0.6% in both groups combined), stroke/transient ischaemic attack (1.4%), life-threatening bleeding (1.3%), stage 2/3 acute kidney injury (0.7%), and valve-related readmission (1.2%). CONCLUSIONS: Broad implementation of BENCHMARK practices contributes to improving efficiency of TAVI pathway reducing LoS and costs without compromising patient safety.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".