Abstract
OBJECTIVES: Progressive ischemic stroke (PIS) is a severe adverse cerebrovascular event that can occur shortly after an acute ischemic stroke (AIS).The clinical factors that predict PIS remain poorly understood. This study aims to develop a nomogram for predicting PIS following AIS. METHODS: This study retrospectively analyzed clinical data from patients diagnosed with AIS at the Affiliated Hospital of Xuzhou Medical University between 2018 and 2021 who subsequently developed PIS. Risk factors associated with PIS were identified using univariate logistic regression, followed by stepwise multivariate logistic regression to construct a predictive model. The resulting model was then transformed into a nomogram, providing neurologists with a clinically practical tool for rapidly assessing the risk of PIS following AIS. RESULTS: Among 580 patients with AIS, 14.31% developed progressive stroke within 14 days. The data set was split into a training set (70%) and a test set (30%). Univariate analysis identified ten indicators associated with progressive stroke, and multivariate logistic regression in the training set revealed four independent risk factors. A nomogram was developed using R software (version 4.3.2) to predict progressive stroke risk. The Model demonstrated strong performance, with ROC curve AUCs of 0.849 (training set) and 0.829 (test set). The DeLong test showed no significant difference between the data sets (P > 0.05), confirming robustness. The overall AUC was 0.974, and the Hosmer-Lemeshow test indicated good calibration (P = 0.887). The calibration plot's mean absolute error was 0.012, and decision curve analysis confirmed the nomogram's clinical utility. Internal validation showed close agreement between the training and test sets. CONCLUSIONS: The nomogram model appears to enhance the prediction of progressive stroke risk in patients with AIS, potentially supporting neurologists in making more informed and timely clinical decisions.