Outcomes of mechanical thrombectomy after recent cardiovascular procedures: a multicenter descriptive cohort
Bibliographic record
Abstract
BACKGROUND: Large vessel occlusions (LVOs) in ischemic stroke represent a critical challenge, particularly in the peri-procedural setting of cardiovascular procedures (CVPs). Mechanical thrombectomy (MT) is a well-established treatment for LVOs; however, its outcomes in patients following recent CVPs remain unclear. OBJECTIVE: This multicenter retrospective cohort study aimed to evaluate the outcomes of MT in patients with ischemic stroke occurring within 30 days of CVPs. METHODS: We analyzed data from four centers, including patients aged ≥18 years who underwent MT for LVO within 30 days of CVPs. CVPs included both surgical (e.g. CABG, valve replacement) and minimally invasive procedures (e.g. TAVR, PCI). Baseline characteristics, procedural metrics, and outcomes, including NIHSS and mRS scores, were collected. Statistical analyses were performed using R software. RESULTS: Of 8,947 screened stroke patients, 27 met the inclusion criteria. The median age was 69 years (IQR 60-83), and 56% were male. Anterior circulation occlusions were present in 93% of cases, with a median baseline NIHSS score of 18 (IQR 14-21). Successful reperfusion (TICI ≥2b) was achieved in 85% of cases, with a median of one thrombectomy pass. At 90 days, 30% of patients achieved functional independence (mRS 0-2), while the mortality rate was 44%. Procedural complications were rare (3.7%). CONCLUSION: MT in patients with peri-procedural LVO after CVPs demonstrates success but poor functional recovery compared with baseline. These findings highlight the need for prospective studies to identify patients who may benefit most from MT in this high-risk population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".