Abstract 42: Blood Biomarkers Refect Tissue Viability in Acute Ischemic Stroke
Bibliographic record
Abstract
Introduction: Assessment of tissue viability, usually performed by multimodal neuroimaging, allows the use of reperfusion therapies in selected patients, even out of the therapeutic time-window. However, multimodal imaging is still a scarce and expensive tool. The availability of blood biomarkers reflecting tissue viability could be a useful tool to manage reperfusion therapies. We aimed to test whether selected candidate biomarkers may reflect tissue viability in relation to Alberta Stroke Program Early CT score (ASPECTS) and multimodal imaging. Methods: The StrokeChip was a prospective, observational study, conducted at six Hospitals in Catalonia. Patients with suspected stroke were enrolled at Emergency Departments. Blood samples were obtained within the first six hours after symptoms onset to measure a 21-biomarker panel. Acute brain neuroimaging was dichotomized into normal (ASPECTS=10) or pathological (ASPECTS<10). For those patients with perfusion imaging, comparison was performed between patients with and without significant mismatch (>20%). Results: From August-2012 to December-2013, 941 out of 1308 patients were ischemic strokes. ASPECTS was obtained in admission neuroimaging in 927 patients. Among them, 25% displayed pathological neuroimaging. Levels of Apo-CIII disclosed a positive correlation with ASPECTS, while negative correlations were found for D-dimer, IL-6, GroA, NT-proBNP and IGFBP-3. In logistic regression analysis, Apo-CIII [OR=0.52(0.36-0.75)], D-dimer [OR=2.47(1.39-4.39)] and IGFBP-3 [OR=2.28(1.51-3.43)] were independently associated with ASPECTS <10, after adjustment by age, sex and NIHSS. Moreover, in 103 patients with baseline perfusion imaging (70% with mismatch >20%), Apo-CIII was an independent predictor of the presence of mismatch after adjustment by age, sex and NIHSS [OR=0.27(0.10-0.75)]. Conclusions: Assessment of tissue viability with blood biomarkers seems feasible. Apo-CIII might represent a surrogate marker for tissue viability assessment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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".