P-006 evaluation of baseline ct aspects and admission neutrophil-lymphocyte ratio in perfusion-guided selected ica/mca stroke patients for endovascular reperfusion therapy
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".