E-033 Current international practice patterns in endovascular techniques used in the treatment of cerebral venous thrombosis
Bibliographic record
Abstract
Introduction Cerebral venous thrombosis (CVT) accounts for approximately 0.5–1% of all strokes. Current treatment for CVT is consensus guided, with unfractionated or low-molecular-weight heparin recommended in the acute management. The role of endovascular therapy (EVT) is less well-defined. A randomized trial, TO-ACT, and a propensity score analysis from the ACTION-CVT trial did not demonstrate a survival or functional benefit for EVT in CVT. Decision-making around EVT in the treatment of CVT remains case-by-case and current practice patterns are not well known. Therefore, we sought to characterize current international practice patterns for EVT in CVT, and explore regional variability. Methods A comprehensive survey was distributed to stroke physicians, interventional neurologists, interventional neuroradiologists, and endovascular neurosurgeons through local networks and professional societies in 2023. The 53-question survey asked about practice patterns and EVT techniques used in the management of CVT. Results Overall, 863 physicians completed the survey with a response rate of 31.4%. Herein, we present data from the 299 neurointerventionalists respondents comprising 158 (52.8%) interventional neurologists, 97 (32.4%) interventional neuroradiologists, 28 (9.4%) endovascular neurosurgeons, and 16 (5.4%) interventional radiologists completed the survey. Respondents were predominantly male (82.6%) and practicing in an academic hospital with a comprehensive stroke center (73.6%). Respondents practiced in North America (12.7%), Asia (58.9%), Europe (25.1%), South America (1.7%), Africa (1.0%) and Oceania (0.7%). In the past three years, 61.9% of respondents performed EVT for the treatment of CVT. Most respondents had performed 2 to 5 cases (51.9%), while only 8.6% had performed more than 10 cases in the past three years. The majority of respondents (85.6%) felt the superficial dural sinuses were amenable to intervention, 34.1% felt the deep venous sinuses were amenable and 9.4% perceived the superficial cortical veins were amenable. Mechanical thrombectomy with aspiration was the most utilized technique with 56.2% of respondents using it in the past three years. The other most utilized techniques were mechanical thrombectomy with stent retriever (50.5%), direct thrombolysis with tissue plasminogen activator (33.4%), direct administration of heparin (32.8%) and balloon angioplasty (23.4%). There was significant regional variability in the EVT techniques used (p<0.001). Direct administration of heparin was more common in Asia, with 46.6% respondents in Asia reporting its use in the past three years. Mechanical thrombectomy with stent retriever or aspiration, and thrombus maceration with wire, were used less commonly in Asia compared to North American and European neurointerventionalists. Eighty percent of neurointerventionalists agreed that in certain situations, EVT was superior to standard medical management of CVT, and 72.6% supported future trials of EVT in the management of CVT. Conclusions In our international survey, more than half of neurointerventionalists have treated CVT with endovascular therapy in the past three years. Mechanical thrombectomy with aspiration or stent retriever are the most commonly used techniques, however, there is regional variability. Overall, the use of endovascular therapy in the management of CVT is rare. These data may inform the design of future clinical trials to guide practice. Disclosures A. Rebchuk: None. B. Brakel: None. J. Ospel: None. Y. Chen: None. M. Heran: None. M. Goyal: None. M. Hill: None. Z. Miao: None. X. Huo: None. Y. Chen: None. S. Sacco: None. S. Yaghi: None. M. Ton: None. G. Thomalla: 2; C; Acandis, Alexion, Amarin, Astra Zeneca, Bayer, Boehringer Igelheim, BristolMyersSquibb/Pfizer, Daiichi Sanyo, Stryker. G. Boulouis: None. H. Yamagami: 1; C; Bristol-Myers Squibb. 3; C; Stryker, Medtronic, J&J, Bayer, Daiichi Sankyo, Bristol-Myers Squibb, Otuska Pharmaceutical. W. Hu: None. S. Nagel: 2; C; Brainomix. 3; C; Boehringer Ingelheim, Pfizer. V. Puetz: None. E. Kristoffersen: None. J. Demeestere: None. Z. Qiu: None. M. Abdalkader: None. S. Al Kasab: None. J. Siegler: 1; C; National Institutes of Health (R61NS135583). 2; C; AstraZeneca. 6; C; Philips, Viz.ai, Philips, Medtronic. D. Strbian: 1; C; Boehringer Ingelheim. 2; C; Orion, Herantis Pharma, CSL Behring. 6; C; Boehringer Ingelheim, Alexion/Astra Zeneca, BMS/Janssen. U. Fischer: 1; C; Medtronic, Stryker, Rapid medical, Penumbra, Boehringer Ingelheim. 2; C; Medtronic, Stryker, CSL Behring. 3; C; Alexion/Portola, Boehringer Ingelheim, Biogen, Acthera. J. Coutinho: 1; C; Bayer, Astrazeneca. 4; C; TrianecT. A. Munckhof: None. D. de Sousa: None. B. Campbell: None. J. Raymond: None. X. Ji: None. G. Saposnik: None. T. Nguyen: 2; C; Brainomix. T. Field: None.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".