Abstract 3548: Neuroimaging Modalities Predict Robust Collaterals For Optimal Intraarterial Acute Stroke Therapy
Bibliographic record
Abstract
BACKGROUND: Collaterals during intra-arterial (IA) therapy have been shown to impact outcome. We sought to identify pre-treatment predictors of robust collaterals to improve patient selection for this therapy. METHODS: With institutional review board approval, we retrospectively reviewed patients from a single center receiving IA therapy for internal carotid artery (ICA) and middle cerebral artery (MCA) M1 segment occlusions from January 2009 to June 2011. We collected the following clinical data: age, gender, stroke score, and risk factors. Radiologic parameters included the pre-treatment Alberta Stroke Program Early CT Score (ASPECTS) on non-contrast CT (NCCT) and CT Perfusion (CTP) cerebral blood volume maps and site of occlusion. All patients had complete diagnostic angiography prior to IA therapy. Collateral circulation of the symptomatic MCA territory was based on a previously published protocol in which any collateral filling of the MCA M1 segment was scored 1, any filling up to the M2 segments was scored 2, any filling up to the M3 segments was scored 3, any filling up to the M4 segments was scored 4. Absent collaterals were scored 5. The endpoint was robust collaterals defined as a score < 4. Associations between demographic, clinical, and radiologic parameters and robust collaterals were determined using chi-squared analysis. Receiver-operating characteristic (ROC) curves determined sensitivities and specificities for significant predictors. RESULTS: Fifty-three patients were identified (mean age of 66 %/- 11 years, median stroke score of 16.5). Occlusions occurred in the MCA M1 segment (37), ICA origin (2), intracranial ICA (9), and tandem ICA origin with intracranial ICA or M1 MCA (5). Robust collaterals were identified in 42 patients and predicted by 2 correlates on multivariate analysis: NCCT ASPECTS > 8 (p = .011), where 89.2% of patients with NCCT ASPECTS > 8 had robust collaterals, compared to 56.3% of patients with NCCT ASPCECTS ≤ 8; and CTP ASPECTS > 7 (p < 0.001), where 100% of patients with CTP ASPECTS > 7 had robust collaterals, compared to 45.5% of patients with CTP ASPCECTS ≤ 7. NCCT ASPECTS > 8 had a sensitivity of 79% and a specificity of 64% in predicting robust collateral circulation. CTP ASPECTS > 7 had a sensitivity of 83% and a specificity of 100%. CONCLUSIONS: Higher ASPECT scores on baseline CT and CTP reflects presence of robust collaterals and can aid in patient selection for IA therapy. In addition, a protocol using CTP may further increase the selection accuracy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".