Predicting the risk of cerebral ischemia symptoms in patients with carotid artery stenosis: development and assessment of a new predictive nomogram

Background Carotid artery stenosis is a significant contributor to cerebral ischemic symptoms, including ischemic stroke and transient ischemic attack (TIA). Nevertheless, the exact reason for the occurrence of cerebral ischemia symptoms in patients with carotid artery stenosis is unclear. This necessitates the development of predictive models using early clinical data to identify patients at high risk for such symptoms. Purpose The aim of this study is to develop and internally validate a risk nomogram for the occurrence of cerebral ischemic symptoms in patients with carotid artery stenosis. Patients and methods: We developed a predictive model based on the clinical data of 437 patients with carotid artery stenosis who were diagnosed for the first time between 2019 and 2022. The patients were randomly divided into a primary cohort (n=306) and a validation cohort (n=131), with a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) regression model was used to optimize feature selection for the prediction model. Multivariable logistic regression analysis was applied to establish the prediction model incorporating the feature selected in the LASSO model. Identification, calibration, and clinical practicability of the predicting model were assessed by the C-index, calibration plot, and decision curve analysis. The data of validation cohort was used to internal validation. Results Predictors contained in the prediction nomogram included systolic blood pressure (SBP), history of operation, smoke, other arteries stenosis, information on lesion site, Platelets (PLT), superoxide dismutase (SOD) and activated partial thromboplastin time (APTT). The C-index for the prediction nomogram was 0.762 (95% CI: 0.710–0.814) for the primary cohort and 0.729 (95% CI: 0.677–0.781) for the validation cohort, which proved good calibration ability in the cohort respectively. The decision curve showed that when the threshold probability was more significant than 30.0% in the prediction model and the maximum net benefit rate was 47.9%, which suggested that patients benefit significantly from our prediction model. Conclusions This novel nomogram incorporating the SBP, history of operation, smoke, other arteries stenosis, information on lesion site, PLT, SOD and APTT could be conveniently used for predicting the risk of cerebral ischemic symptoms in individual patients with carotid artery stenosis.