Abstract W P41: Multimodal Imaging ASPECTS: A Predictor Of Good Prognosis Before Thrombectomy
Bibliographic record
Abstract
BACKGROUND: The goal of this study was to determine the accuracy and reliability of noncontrast CT (NCCT), perfusion CT parameters maps (CTP) an CT-angiography source images (CTASI), analyzed with ASPECTS (Alberta Stroke Program Early CT Scale), as final outcome predictors of endovascular stroke treatment. METHODS: Consecutive patients with ischemic stroke who received endovascular reperfusion were retrospectively analyzed (04/2009-06/2014). In acute stroke patients NCCT, CTP-CBV (maps of Cerebral Blood Volume) and CTASI was performed according to our hospital guidelines before thrombectomy. We independently applied the ASPECTS in all baselines NCCT, CBV-CTP, CTASI and follow-up NCCT 24H. Receiver-operating characteristic curve (ROC) analysis was performed to determine optimal thresholds for imaging parameters to predict dichotomized good clinical outcome (modified Rankin score ≤2, mRS). RESULTS: From a database containing 402 thrombectomies, 94 underwent CTP and CTA for acute cerebrovascular ischemia. Ninety-one with MCA or ICA occlusion were identified for our study (49% women, mean age 64+/-13 years; NIHSS at admission 17). Successful recanalization (TICI 2b-3) was achieved in 73/91 patients (80%). In ROC analysis higher CBV ASPECTS was associated with 90-day mRS score 0-2 (Area Under Curve=0.72;95% CI, 0.6-0.8; P:0.001) with optimal threshold of 8 (Sensivity 83%, Specificity 77%). The CTASI ASPECTS was highly correlated with 24h NCCT ASPECTS (r: 0.65;Spearman p:0.001). Favorable clinical outcomes (mRS 0-2) was greater in patients with CBV ASPECTS >8 compared with ≤8 (71% vs 37%;p:0.001) and CTACI ASPECTS>8 compared with ≤8 (68% vs 37%;p=0.02). In multivariate logistic regression CBV ASPECTS was associated with significant functional and radiological outcomes after adjustment for age, sex, time from CT to groin-puncture and baseline NIHSS (OR: 2.5; 95%CI:1.8-3.5, p:0.001). CONCLUSIONS: These results suggest that CBV ASPECTS approach can predict a favorable clinical outcome after endovascular treatment with good sensitivity and specificity. CTASI ASPECTS may be more sensitive for detection of infarction and more accurate for predicting the final infarct size however not to predict clinical outcome.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".