Abstract 246: Effect of platelet count on outcome in ischemic stroke patients with LVO undergoing MT;a meta‐analysis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,012 | 0,038 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».