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Updated: May 26, 2026
Optimized Management of Endovascular Treatment for Acute Ischemic Stroke
Published on: January 18, 2018
Preoperative multimodal CT for selection of acute anterior circulation occlusion stroke patients for mechanical
Guiyun Luo1, Manhong Deng2, Lilan She3
1Department of Neurology, Sanming First Hospital Affiliated to Fujian Medical University, Sanming, China.
Insights
This study developed multimodal CT-based models to predict prognosis after successful recanalization in acute anterior circulation occlusive stroke. The Clinical-non-Perfusion (C-NP) model demonstrated strong predictive power and practical utility for clinicians.
Area of Science:
- Neurology
- Radiology
- Medical Imaging
Background:
- Acute anterior circulation occlusive stroke is a leading cause of disability.
- Mechanical thrombectomy has improved outcomes but requires accurate prognostic assessment.
- Preoperative imaging plays a crucial role in predicting post-treatment prognosis.
Purpose of the Study:
- To develop and compare preoperative multimodal CT-based models for predicting prognosis after successful recanalization in acute anterior circulation occlusive stroke.
- To identify the most effective model for clinical application in assessing patient outcomes.
Main Methods:
- Development of five multivariate logistic regression models (C-I, C-NP, C-NC, C, I) using preoperative CT data.
- Statistical analysis of clinical and imaging variables to identify significant predictors.
- Model performance evaluation using receiver operating characteristic (ROC) curve analysis, including area under the curve (AUC), sensitivity, and specificity.
- Validation through confusion matrix and 5-fold cross-validation.
Main Results:
- 131 patients with successful recanalization were analyzed.
- Significant predictors included age, preoperative blood glucose, NIHSS, ASPECTS, collateral score, infarct core volume, and hypoperfusion volume.
- The Clinical-non-Angiography (C-NC) model showed the highest AUC (0.865), followed closely by the Clinical-non-Perfusion (C-NP) model (0.861).
- The C-NP model achieved an accuracy of 0.771 and a mean AUC of 0.828 from cross-validation.
Conclusions:
- Multimodal CT-based models, particularly the C-NP model, demonstrate strong predictive power for outcomes after mechanical thrombectomy.
- The C-NP model offers practical utility for clinicians to rapidly assess prognosis post-intervention.
- These models can aid in personalized treatment strategies and patient management.
Objective:
By developing and comparing various preoperative multimodal CT-based models to predict prognosis after successful recanalization in acute anterior circulation occlusive stroke, this study aimed to ascertain the prognostic assessment of patients following successful recanalization.
Methods:
Patients with acute anterior circulation large vessel occlusion who underwent mechanical thrombectomy and achieved successful recanalization were consecutively enrolled from Sanming First Hospital Affiliated to Fujian Medical University between January 2022 and October 2024. Based on the 90-day modified Rankin Scale (mRS) scores, the patients were categorized into a favorable clinical outcome group (mRS 0-3) and an unfavorable clinical outcome group (mRS 4-6). Following the identification of statistically significant variables from the analyzed clinical and imaging data, optimal cut-off values were computed. Five multivariate logistic regression models were subsequently constructed: Clinical-Imaging (C-I), Clinical-non-Perfusion (C-NP), Clinical-non-Angiography (C-NC), Clinical-only (C), and Imaging-only (I). Models performance were compared using receiver operating characteristic (ROC) curve analysis, with comparisons based on the area under the curve (AUC), sensitivity, and specificity. Using the Delong test for model comparison and selection, a nomogram of the optimal model was developed. Model performance was subsequently assessed by means of a confusion matrix and 5-fold cross-validation.
Results:
Of the 131 enrolled patients, 77 (58.78%) were classified into the favorable clinical outcome group and 54 (41.22%) into the unfavorable clinical outcome group. Statistically significant differences were identified in age (59.5 years), preoperative blood glucose (PBG) (7.21 mmol/L), National institutes of health stroke scale (NIHSS) (18.5), Alberta Stroke Program Early CT Score (ASPECTS) (7.5), collateral score (3.5), infarct core volume (19.25 mL), and hypoperfusion volume (180.65 mL). The AUCs for the five models were C-I Model (0.851, 95% CI: 0.785-0.917), C-NC Model (0.865, 95% CI: 0.804-0.926), C-NP Model (0.861, 95% CI: 0.798-0.923), C Model (0.713, 95% CI: 0.626-0.801) and I Model (0.772, 95% CI: 0.688-0.855). The accuracy of the optimal C-NP Model was 0.771, and the mean AUC from 5-fold cross-validation was 0.828.
Conclusion:
The combined model revealed strong predictive power regarding outcomes following successful mechanical thrombectomy. Furthermore, the C-NP Model holds greater practical utility, as it enables clinicians to quickly assess the prognosis after a successful intervention.
