Nomogram for predicting asymptomatic intracranial atherosclerotic stenosis in a neurologically healthy population

Abstract

Abstract Asymptomatic intracranial atherosclerotic stenosis (aICAS) is a major risk factor for cerebrovascular events. The study aims to construct and validate a nomogram for predicting the risk of aICAS. Participants who underwent health examinations at our center from September 2019 to August 2023 were retrospectively enrolled. The participants were randomly divided into a training set and a testing set in a 7:3 ratio. Firstly, in the training set, least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were performed to select variables that were used to establish a nomogram. Then, the receiver operating curves (ROC) and calibration curves were plotted to assess the model’s discriminative ability and performance. A total of 2563 neurologically healthy participants were enrolled. According to LASSO-Logistic regression analysis, age, fasting blood glucose (FBG), systolic blood pressure (SBP), hypertension, and carotid atherosclerosis (CAS) were significantly associated with aICAS in the multivariable model (adjusted P < 0.005). The area under the ROC of the training and testing sets was, respectively, 0.78 (95% CI: 0.73–0.82) and 0.65 (95% CI: 0.56–0.73). The calibration curves showed good homogeneity between the predicted and actual values. The nomogram, consisting of age, FBG, SBP, hypertension, and CAS, can accurately predict aICAS risk in a neurologically healthy population.

Keywords

• Asymptomatic intracranial atherosclerotic stenosis
• Nomogram
• LASSO-Logistic regression
• Neurologically healthy population