Successful Reperfusion is Associated with Favorable Functional Outcome despite Vessel Perforation during Thrombectomy: A Case Series and Systematic Review
Bibliographic record
Abstract
BACKGROUND: Arterial perforation is a potentially serious complication during endovascular thrombectomy. PURPOSE: Our aim was to describe interventional approaches after arterial perforation during endovascular thrombectomy and to determine whether reperfusion remains associated with favorable outcome despite this complication. DATA SOURCES: Data from consecutive patients with acute stroke undergoing endovascular thrombectomy were retrospectively collected between 2015 to 2020 from a single-center cohort, and a systematic review was performed using PubMed, EMBASE, and Ovid MEDLINE up to June 2020. STUDY SELECTION: Articles reporting functional outcome after arterial perforation during endovascular thrombectomy were selected. DATA ANALYSIS: Functional outcomes of patients achieving successful reperfusion (TICI 2b/3) were compared with outcomes of those with unsuccessful reperfusion in our single-center cohort. We then summarized the literature review to describe interventional approaches and outcomes after arterial perforation during endovascular thrombectomy. DATA SYNTHESIS: In our single-center cohort, 1419 patients underwent endovascular thrombectomy, among whom 32 (2.3%) had vessel perforation and were included in the analysis. The most common hemostatic strategy was watchful waiting (71% of cases). Patients with successful reperfusion had a higher proportion of favorable 90-day mRS scores (60% versus 12.5%; P = .006) and a lower mortality rate (13.3% versus 56.3%, P = .01) than patients without successful reperfusion. Thirteen articles were included in the systematic review. Successful reperfusion also appeared to be associated with better outcomes. LIMITATIONS: Given the low number of published reports, we performed only a descriptive analysis. CONCLUSIONS: Arterial perforation during endovascular thrombectomy is rare but is associated with high mortality rates and poor outcome. However, successful reperfusion remains correlated with favorable outcome in these patients.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".