Abstract TP37: Predictors of Hemorrhage After Endovascular Therapy: Findings From the Interrsect Study
Bibliographic record
Abstract
Background: While recent endovascular therapy trials have had a minimal number of adverse events, intracerebral hemorrhage (ICH) still occurs. The predictors of ICH with endovascular therapy remain unclear. We assessed predictors of hemorrhage following endovascular thrombectomy using data from the prospectively collected, multicenter INTERRSeCT study. Methods: Patients undergoing endovascular therapy +/- intravenous alteplase (tPA) were enrolled and received baseline CT/CTA, follow-up CTA/Angiogram and 24-hr CT or MRI images. Primary outcome was any ICH as per the ECASS classification of hemorrhage. Secondary outcome was PH1/PH2 hemorrhages. We assessed the relations between ICH and baseline ASPECTS scoring, thrombus location, residual flow, collateralization, tPA use, and final recanalization state. Multivariable regression with stepwise selection was used to adjust for relevant covariates. Results: Of 242 patients who met inclusion criteria, 58 (24%) had an ICH at 24 hours (HI1 53%, HI2 19%, PH1 7%, PH2 21%). Post-procedure hemorrhage was associated with lower ASPECTS scores (p<0.001), ICA (p=0.004), proximal M1 (p=0.008), and mid-M1 (p=0.002) thrombus locations, and serum glucose (7.6 vs. 6.7; p=0.027). When adjusted for covariates, lower ASPECTS score (OR: 1.41 per point lost; 95% CI: 0.57-0.88; p=0.002), mid-M1 thrombus location (OR: 2.03; 95% CI: 1.03-4.01; p=0.041), and serum glucose (OR:1.15, 95% CI: 1.01-1.35, p=0.033) independently predicted the presence of post-procedure ICH. PH1/PH2 hemorrhages were associated with ICA thrombus (OR:2.96, 95% CI:1.05-8.33, p=0.04) after adjusting for relevant covariates. Conclusion: Early ischemia defined by imaging, mid-M1 thrombus location, and increased serum glucose are associated with increased risk of hemorrhage in patients undergoing combination tPA and endovascular therapy.
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".