Abstract 246: Effect of platelet count on outcome in ischemic stroke patients with LVO undergoing MT;a meta‐analysis
Bibliographic record
Abstract
Introduction Large randomized clinical trials have established the superiority of Mechanical Thrombectomy (MT) for the treatment of Large Vessel occlusion (LVO) in patients with Acute Ischemic stroke (AIS) in terms of revascularization rates and clinical outcomes as compared to IV‐tPA alone.(1, 2, 3) However, Data regarding outcomes of mechanical thrombectomy in patients with low platelets is limited and shows conflicting results. (4, 5, 6, 7, 8) Methods This meta‐analysis was performed according to the preferred reporting items for systematic review and meta‐analysis (PRISMA) guidelines.(9) We defined thrombocytopenia as platelet count < 150,000 /μL. We further classified these patients into two groups: 1) Mild thrombocytopenia (Platelet count 100,000 – 149,000 /μL) and 2) Moderate to severe thrombocytopenia (Platelet count <100,000 /μL). The favourable outcome was 90‐day functional independence, designated as an Modified Rankin Score (MRS) ≤ 2 at 90 days. Unfavorable outcomes were 1) Symptomatic Intracranial hemorrhage (sICH) and 2) Mortality at 90 days. Results All studies were determined to be of good/high quality (Newcastle Ottawa scale).(10) Compared to patients with normal platelets, Patients with low platelets (< 150,000 /μL) had a statistically significant worse outcome (mRS >2 )at 90 days (RR 0.80 [95% CI: 0.69 ‐ 0.94] p = 0.006). However, on analysis based on different platelet cutoff values, the difference in mRS score was not statistically significant in patients with Mild low platelets (100,000 – 149,000 /μL) (RR 0.84 [95% CI: 0.70 ‐ 1.00] p = 0.05) and moderate/severely low platelets (< 100,000 /μL) (RR 0.71 [95% CI: 0.48 ‐ 1.06] p = 0.09) when compared to patients with normal platelets. Similarly, Mortality was significantly increased in patients in the low platelet group (RR 1.95 [95% CI: 1.62 ‐ 2.36] p < 0.00001). Mortality was also significantly increased in the mild low platelet subgroup (RR 1.88 [ 95% CI: 1.34 ‐ 2.63] p = 0.0002) and moderate/severe low platelet subgroup (RR 2.07 [95% CI: 1.46 ‐ 2.92] p < 0.0001) when compared to patients with normal platelets. Furthermore, sICH events were significantly increased in patients in the low platelet group (RR 2.47 [95% CI: 1.51 ‐ 4.05] p = 0.0003). Similarly, on analysis based on different platelet cutoff values, sICH was also significantly increased in the mild low platelet subgroup (RR 2.34 [ 95% CI: 1.24 ‐ 4.40] p = 0.008) and moderate/severe low platelet subgroup (RR 4.13 [95% CI: 2.00 ‐ 8.50] p = 0.0001). Conclusion Our meta‐analysis revealed that compared to individuals with normal platelet count, those with platelet count (< 150,000 /μL) had worse functional outcomes (MRS ≤ 2) , higher mortality rates, and a greater incidence of sICH. However, on analysis based on different platelet cutoffs, difference in MRS score was insignificant while the mortality rates and sICH incidence remained significantly higher. This may be attributed to smaller number of studies in the “cut‐off” groups leading to decreased power. Nevertheless, our findings suggest that MT for LVO in patients with low platelets leads to worse outcomes, however these risk were not significant in group with mild thrombocytopenia. Hence, Clinicians should carefully monitor for these risks while performing procedure or post procedure care.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.038 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".