Ultrasound-Guided Transfemoral Access for Coronary Procedures: A Pooled Learning Curve Analysis From the FAUST and UNIVERSAL Trials
Bibliographic record
Abstract
BACKGROUND: The learning curve for new operators performing ultrasound-guided transfemoral access (TFA) remains uncertain. METHODS: We performed a pooled analysis of the FAUST (Femoral Arterial Access With Ultrasound Trial) and UNIVERSAL (Routine Ultrasound Guidance for Vascular Access for Cardiac Procedures) trials, both multicenter randomized controlled trials of 1:1 ultrasound-guided versus non-ultrasound-guided TFA for coronary procedures. Outcomes included the composite of major bleeding or vascular complications and successful common femoral artery cannulation. Participants were stratified by the operators' accrued case volume. We used adjusted repeated-measurement logistic regression, with random intercepts for operator clustering, for comparison against the non-ultrasound-guided TFA group and to model the learning curve. RESULTS: The FAUST and UNIVERSAL trials randomized a total of 1624 patients, of which 810 were randomized to non-ultrasound-guided TFA and 814 to ultrasound-guided TFA (cases 1-10, 391; 11-20, 183; and >20, 240). Participants who had operators who performed >20 ultrasound-guided TFAs had a decreased risk for the primary end point (5/240 [2.1%] versus 64/810 [7.9%]; adjusted odds ratio, 0.26 [95% CI, 0.09-0.61]) compared with non-ultrasound-guided TFA. Operators who performed >20 ultrasound-guided procedures had increased odds of successfully cannulating the common femoral artery (224/246 [91.1%] versus 327/382 [85.6%]; adjusted odds ratio, 1.76 [95% CI, 1.08-2.89]) compared with non-ultrasound-guided TFA. The learning curve plots demonstrated growing competence with increasing accrued cases. CONCLUSIONS: New operators should perform at least 20 ultrasound-guided TFA to decrease access site complications and increase proper cannulation compared with non-ultrasound-guided TFA. Additional accrued cases may lead to increased proficiency. Training programs should consider these findings in the transradial era.
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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 | 0.098 | 0.160 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.034 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".