E-232 Predictors of poor clinical outcomes in patients with distal medium vessel occlusions: a retrospective, multicenter, and multinational study
Bibliographic record
Abstract
<h3>Background</h3> Acute ischemic stroke (AIS) significantly contributes to global morbidity and mortality, with distal medium vessel occlusions (DMVO) accounting for a substantial portion of AIS cases. This study investigates the predictors of very poor functional outcomes in AIS patients due to DMVOs. <h3>Methods</h3> In this retrospective, multicenter, multinational study, data were collected from 37 academic centers across North America, Asia, and Europe. The cohort included 1,490 patients with AIS due to MeVO, treated with mechanical thrombectomy (MT) or MT plus intravenous thrombolysis (IVtPA) between September 2017 and July 2021. The primary outcome measured was a very poor clinical outcome, defined as a modified Rankin Scale (mRS) score of 5–6. Logistic regression analyses identified predictors of these outcomes. <h3>Results</h3> Of the 1,490 patients, 1,164 (78.1%) had mRS scores of 0–4, while 326 (21.9%) experienced poor outcomes (mRS scores 5–6). Significant predictors of poor outcomes included older age (OR: 1.03, CI: 1.02 to 1.04, p<0.001), higher baseline NIHSS scores (OR: 1.09, CI: 1.07 to 1.12,p<0.001), a baseline mRS of 4 (OR: 4.53, CI: 1.97 to 10.4, p<0.001), Tmax volume of 4 (OR:1.00, CI: 1.00 to 1.01, p=0.022), and the occurrence of any type of intracranial hemorrhage (OR:1.77, CI: 1.31 to 2.38, p<0.001). Successful recanalization (TICI 2b-3) was associated with a significant decrease in the odds of very poor outcomes (OR: 0.28, CI: 0.19 to 0.39, p<0.001). The multivariable logistic regression model demonstrated excellent predictive accuracy (AUC0.89 [95% CI 0.84 - 0.93], p <0.01). <h3>Conclusion</h3> This study identifies key predictors of poor functional outcomes in AIS patients with DMVO, emphasizing the importance of age, baseline NIHSS scores, pre-morbid mRS, Tmax>4 seconds volume, and presence of intracranial hemorrhage. These insights are crucial for developingtargeted strategies for managing DMVO patients. The study’s findings also highlight the need forfurther research to optimize treatment personalization and outcome prediction in this patient population. <h3>Disclosures</h3> <b>B. Musmar:</b> None. <b>H. Salim:</b> None. <b>S. Ghozy:</b> None. <b>A. Guenego:</b> None. <b>N. M Cancelliere:</b> None. <b>V. Mendes Pereira:</b> None. <b>P. Jabbour:</b> None. <b>A. A Dmytriw:</b> None. <b>V. Yedavalli:</b> None.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".