P-006 evaluation of baseline ct aspects and admission neutrophil-lymphocyte ratio in perfusion-guided selected ica/mca stroke patients for endovascular reperfusion therapy
Notice bibliographique
Résumé
Introduction The neutrophil-lymphocyte ratio (NLR) as an inexpensive and easily available inflammatory marker, has become a useful index in various conditions such as cardiovascular diseases, neoplastic diseases, diabetes, and recently acute ischemic stroke (AIS). We studied the association between the baseline Alberta Stroke Program Early Computed Tomographic Score (ASPECTS) with admission NLR in AIS patients with anterior circulation large vessel occlusion who underwent perfusion imaging to be selected for IA reperfusion therapy based on perfusion imaging profile. Methods This is an IRB approved ongoing retrospective study. Consecutive AIS patients with CTA/MRA verified ICA/MCA occlusion presenting in acute setting (<8 h) were studied. Exclusion criteria included: AIS due to other vascular pathologies, patients who were hospitalized before developing stroke, receiving corticosteroids, and associated systemic or infectious diseases prior to developing stroke. Admission NLR was obtained using baseline white blood cell differential by dividing the percentage of neutrophils by the percentage of lymphocytes. ASPECTS were obtained after two blinded observers evaluated the baseline CT examinations. Following interobserver agreement evaluation, ASPECTS was dichotomized into >7 (favorable) and ≤7 (unfavorable) groups. Accordingly, NLR was sub-classified into high (>5), and low (≤5) groups. Patient demographics, baseline NIHSS score, medical risk factors, and final clinical outcome (90-day mRS scores) were obtained subsequently. Intraclass Correlation Coefficient (ICC) was used to evaluate interobserver agreement. Chi-square, Mann-Whitney U, and student t tests were used for univariate analyzes as appropriate. Correlation between ASPECTS and NLR was calculated using Pearson9s correlation coefficient (r). The receiver operating characteristic (ROC) curve analysis was performed to determine the optimal cut-off NLR for discriminating favorable ASPECTS. P < 0.05 was considered to be statistically significant. Results Sixty five patients (33 F/32 M, mean age ±SD of 69.77 ± 14.87), with ICA/MCA occlusion were enrolled to the study. Interobserver agreement for ASPECTS was good (ICC=0.82). After dichotomizing the patients based on the ASPECTS and NLR, all groups were comparable in terms of demographics and medical risk factors (P > 0.05). In 52 patients who were selected for IA therapy based on perfusion imaging profile, a significant inverse correlation was observed between ASPECTS and admission NLR (P = 0.01, r=-0.33). In selected patients, unfavorable ASPECTS (≤7) was significantly associated with high NLR (>5) (P = 0.002) and ROC curve analysis revealed that NLR of 5 or less can discriminate favorable (>7) from unfavorable (≤7) ASPECTS with the sensitivity of 80.8% and specificity of 65.4% (P = 0.009, area under the curve= 0.69). In 13 cases who were excluded from receiving IA reperfusion therapy based on their perfusion imaging profile such correlation and association between ASPECTS and NLR were not observed (P = 0.06 and P = 0.93, respectively) and the discriminative power of NLR was poor for prediction of favorable ASPECTS (P = 0.40). Overall, neither ASPECTS nor NLR was predictor of final good functional outcome (P = 0.25, P = 0.96, respectively). Conclusions In our cohort, baseline CT ASPECTS inversely correlated with admission NLR in anterior circulation AIS patients who had favorable perfusion imaging profile for IA reperfusion therapy. Additionally, in this subgroup of patients, NLR of 5 or less discriminated favorable from unfavorable ASPECTS. Disclosures A. Honarmand: None. A. Shaibani: None. F. Syed: None. A. Elmokadem: None. M. Hurley: None. S. Ansari: None.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».