Endovascular Thrombectomy Technique Optimization: A SVIN Registry Analysis
Bibliographic record
Abstract
Background: Achieving excellent recanalization (Modified Thrombolysis in Cerebral Infarction 2c/3) in fewer attempts improves clinical outcomes. Previous studies suggest that switching techniques after a failed first pass may enhance reperfusion rates. This study evaluates whether technique switching improves subsequent reperfusion in a large multicenter registry. Methods: We analyzed retrospective and prospective SVIN (Society of Vascular and Interventional Neurology) registry data from 12 US centers (October 2014-December 2021) involving endovascular therapy for M1 or internal carotid artery-terminus (ICA-T) occlusions. Patients with at least 2 recanalization attempts using stent retriever (SR), contact aspiration (CA), or combined technique (CT) were included. Primary outcome was the likelihood of achieving TICI 2c/3 reperfusion with or without technique switching on the second pass. Secondary outcomes included the likelihood of final TICI 2c/3 stratified by the technique and occlusion location. Results: Among 2893 endovascular therapy treatments, 1089 patients (37.6%) had successful reperfusion after the first pass. First-pass TICI 2c/3 rates for ICA-T occlusions were 36.0% with SR, 23.6% with CA, and 35.8% with CT; for M1 occlusions, the rates were 38.8% with SR, 39.3% with CA, and 38.6% with CT. A total of 1420 treatments included at least 2 passes. ICA-T occlusions occurred in 20.4% and M1 occlusions in 79.6%. In multivariable analysis, in M1 occlusions, switching from CT to alternative technique after a failed first pass significantly increased the odds of achieving TICI 2c/3 after the second pass (adjusted odds ratio, 2.08 [95% CI, 1.18-3.67]). Patients who had 2 failed attempts using CA had significantly higher odds of achieving final TICI 2c/3 compared with those with 2 failed passes using the SR technique (adjusted odds ratio 1.65, [95% CI, 1.09-2.51]). Conclusion: In M1-middle cerebral artery occlusion, switching from CT to SR or CA was associated with an improvement in TICI2c/3 rates on the second pass. In addition, after 2 failed passes with CA, additional passes increased the odds of achieving complete reperfusion compared with SR.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".