EP51* Burden of<i>tough to retrieve</i>clots in acute ischemic stroke: clinical and economic impact of increasing retrieval attempts in mechanical thrombectomy
Bibliographic record
Abstract
Introduction Emerging data show an association between increased mechanical thrombectomy (MT) passes and poor outcomes in ischemic stroke. Clots that require ≥3 passes are more often tough, fibrin-rich thrombi than those retrieved within two passes. Aims To assess the clinical and economic burden of number of MT passes, we evaluated the odds of achieving successful reperfusion and functional independence in first pass, or 2–3 passes, compared to ≥4 passes. Methods A retrospective observational study was conducted on 857 cases treated with MT from the Irish National Thrombectomy database. Outcomes were 90-day functional independence (mRS 0–2) and successful reperfusion (mTICI 2b-3) stratified by number of passes (1; 2–3; and ≥4) with multivariable regression to adjust for confounding variables. A decision-tree economic model was informed by 90-day mRS: independent (0–2), dependent (3–5), or dead, with literature-derived annual healthcare costs by mRS. Results The odds of achieving successful reperfusion were significantly higher for 1 vs. ≥4 passes (OR 7.19, p<0.001); and for 2–3 vs. ≥4 passes (OR 3.19, p<0.001). The odds of functional independence were significantly higher for 1 vs. ≥4 passes (OR 2.51, p<0.001); and trended higher for 2–3 vs. ≥4 passes (OR 1.49, p=0.199). Patients treated with 1 pass had the lowest annual healthcare costs ($20,910); 2–3 passes ($21,999) ≥4 passes ($25,904). Conclusion Odds of achieving good outcomes decline with MT passes, while care costs increase. Thrombectomy devices that improve interaction with tough clots for rapid and complete retrieval in fewer passes may improve clinical and economic outcomes. References Yoo AJ. Journal of Stroke 2017;19(2):121. Douglas A. JNIS 2020;12(6):557–562. Liebeskind DS. Stroke 2011;42(5):1237–1243. Abbasi M. Interventional Neuroradiology 2021;202115910199211009119. Siddiqui AH. Stroke 2021;52(Suppl_1):AP14–AP14. García-Tornel Á. Stroke 2019;50(7):1781–1788. Shireman TI. Stroke 2017;48(2):379–387. Disclosure Dr. Jack Alderson, Radiology, Beaumont Hospital. Prof. Dr. John Thornton, Neuroradiology, Beaumont Hospital. Consultancy for Johnson & Johnson, Perfuze and Microvention. Shareholder Perfuze. Cindy Tong and Shelly Ikeme are employees of Johnson & Johnson. Heather Cameron is an employee of EVERSANA, a consultant for Johnson & Johnson. This research is funded by Cerenovus – a company of Johnson & Johnson
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".