A novel nomogram based on the patient's clinical data and CT signs to predict poor outcomes in AIS patients.

Abstract

Background: The 2019 American Heart Association/American Stroke Association (AHA/ASA) guidelines strongly advise using non-contrast CT (NCCT) of the head as a mandatory test for all patients with suspected acute ischemic stroke (AIS) due to CT's advantages of affordability and speed of imaging. Therefore, our objective was to combine patient clinical data with head CT signs to create a nomogram to predict poor outcomes in AIS patients.

Methods: A retrospective analysis was conducted on 161 patients with acute ischemic stroke who underwent mechanical thrombectomy at the Guangzhou Hospital of Integrated Traditional and Western Medicine from January 2019 to June 2023. All patients were randomly assigned to either the training cohort (n = 113) or the validation cohort (n = 48) at a 7:3 ratio. According to the National Institute of Health Stroke Scale (NIHSS) score 7 days after mechanical thrombectomy, the patients were divided into the good outcome group (<15) and the poor outcome group (≥15). Predictive factors were selected through univariate analyses, LASSO regression analysis, and multivariate logistic regression analysis, followed by the construction of a nomogram predictive model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of the model, and bootstrapped ROC area under the curve (AUC) estimates were calculated to provide a more stable evaluation of the model's accuracy. The model's calibration performance was evaluated through the Hosmer-Lemeshow goodness-of-fit test and calibration plot, and the clinical effectiveness of the model was analyzed through decision curve analysis (DCA).

Results: Multivariate logistic regression analysis showed that hyperdense middle cerebral artery sign (HMCAS) (OR 9.113; 95% CI [1.945-42.708]; P = 0.005), the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) > 6 (OR 7.707; 95% CI [2.201-26.991]; P = 0.001), NIHSS score (OR 1.085; 95% CI [1.009-1.166]; P = 0.027), age (OR 1.077; 95% CI [1.020-1.138]; P = 0.008) and white blood cell count (WBC) (OR 1.200; 95% CI [1.008-1.428]; P = 0.040) were independent risk factors for early poor outcomes after mechanical thrombectomy. The nomogram model was constructed based on the above factors. The training set achieved an AUC of 0.894, while the validation set had an AUC of 0.848. The bootstrapped ROC AUC estimates were 0.905 (95% CI [0.842-0.960]) for the training set and 0.848 (95% CI [0.689-0.972]) for the validation set. Results from the Hosmer-Lemeshow goodness-of-fit test and calibration plot indicated consistent performance of the prediction model across both training and validation cohorts. Furthermore, the DCA curve demonstrated the model's favorable clinical practicality.

Conclusion: This study introduces a novel practical nomogram based on HMCAS, ASPECTS > 6, NIHSS score, age, and WBC that can well predict the probability of poor outcomes after MT in patients with AIS.