Prognostic biomarkers in ischemic stroke treated with mechanical thrombectomy: a systematic review
Bibliographic record
Abstract
Abstract Mechanical thrombectomy (MT) is a key therapy for acute ischemic stroke (AIS), improving survival and functional outcomes. However, the variability in results highlights the need for predictive markers to refine patient selection. Biomarkers reflecting inflammation and metabolic stress are gaining recognition for their role in AIS and MT outcomes. To systematically review and synthesize the evidence on biomarkers associated with clinical outcomes in AIS patients undergoing MT. Specific aims include evaluating their relationship with functional recovery (mRS), mortality, infarct volume, hemorrhagic transformation, and complications such as malignant brain edema (MBE) and delayed cerebral ischemia (DCI). A systematic review of the literature was conducted in accordance with the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement to identify studies evaluating biomarkers in MT. The PubMed and Embase databases were searched using the following terms: (Marker OR biomarker*) AND (Mechanical Thrombectomy OR endovascular) AND Stroke. Of 2,834 articles identified, 86 met inclusion criteria. Several biomarkers, such as C-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), adenosine deaminase (ADA), neuron-specific enolase (NSE), and matrix metalloproteinase-9 (MMP-9), were consistently associated with worse functional outcomes, increased mortality, and higher risk of complications including hemorrhagic transformation and MBE. Multiple biomarkers demonstrate prognostic value in AIS patients undergoing MT. These findings may support risk stratification and individualized care, though further prospective studies are needed to integrate these biomarkers into the clinical practice.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".