A Nomogram Prediction Model Based on Tissue Window for the Prognosis of Patients with Acute Ischemic Stroke Undergoing Thrombectomy.

Objective: Thrombectomy greatly improves the clinical prognosis of patients with acute ischemic stroke (AIS). The aim of this study is to develop a nomogram model that can predict the prognosis of patients with acute ischemic stroke undergoing thrombectomy.

Methods: We retrospectively collected information of patients with acute ischemic stroke who were admitted to the stroke Green Channel of the First Affiliated Hospital of Soochow University from September 2018 to May 2022. The main outcome was defined as a three-month unfavorable outcome (modified Rankin Scale 3-6). Based on the results of multivariate regression analysis, a nomogram was established. We tested the accuracy and discrimination of our nomogram by calculating the consistency index (C-index) and plotting the calibration curve.

Results: National Institutes of Health Stroke Scale (NIHSS) score (OR, 1.418; 95% CI, 1.177-1.707; ＜0.001), low density lipoprotein cholesterol (LDL-C) (OR, 2.705; 95% CI, 1.203-6.080; = 0.016), Alberta Stroke Program Early Computed Tomography Score (ASPECTS) (OR, 0.633; 95% CI, 0.421-0.952; = 0.028), infarct core volume (OR, 1.115; 95% CI, 1.043-1.192; = 0.001) and ischemic penumbra volume (OR, 1.028; 95% CI, 1.006-1.050; = 0.012) were independent risk factors for poor clinical prognosis of AIS patients treated with thrombectomy. The C-index of our nomogram was 0.967 and the calibration plot revealed a generally fit in predicting three-month unfavorable outcomes. Based on this nomogram, we stratified the risk of thrombectomy population. We found that low-risk population is less than or equal to 65 points, and patients of more than 65 points tend to have a poor clinical prognosis.

Conclusion: The nomogram, composed of NIHSS, LDL-C, ASPECTS, infarct core volume and ischemic penumbra volume, may predict the clinical prognosis of cerebral infarction patients treated with thrombectomy.