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Updated: Jul 28, 2026
Optimized System for Cerebral Perfusion Monitoring in the Rat Stroke Model of Intraluminal Middle Cerebral Artery Occlusion
Published on: February 17, 2013
Development and validation of a nomogram for cerebral hemorrhage in patients with carotid stenosis undergoing
Xianjun Zhang1, Xiaoliang Wang2, Teng Ma3
1Department of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Background:
Hyperperfusion-induced cerebral hemorrhage (HICH) is a rare but severe complication in patients with carotid stenosis undergoing stent placement for which predictive models are lacking. Our objective was to develop a nomogram to predict such risk.
Methods:
We included a total of 1226 patients with carotid stenosis who underwent stenting between June 2015 and December 2022 from three medical centers, divided into a development cohort of 883 patients and a validation cohort of 343 patients. The model used LASSO regression for feature optimization and multivariable logistic regression to develop the predictive model. Model accuracy was assessed via the receiver operating characteristic curve, with further evaluation of calibration and clinical utility through calibration curves and decision curve analysis (DCA). The model underwent internal validation using bootstrapping and external validation with the validation cohort.
Results:
Older age (OR 1.07, p=0.005), higher degrees of carotid stenosis (OR 1.07, p=0.006), poor collateral circulation (OR 6.26, p<0.001), elevated preoperative triglyceride levels (OR 1.27, p=0.041) and neutrophil counts (OR 1.36, p<0.001) were identified as independent risk factors for HICH during hospitalization. The nomogram constructed based on these predictive factors demonstrated an area under the curve (AUC) of 0.817. The AUCs for internal and external validation were 0.809 and 0.783, respectively. Calibration curves indicated good model fit, and DCA confirmed substantial clinical net benefit in both cohorts.
Conclusion:
We developed and validated a nomogram to predict HICH in patients with carotid stenosis post-stenting, facilitating early identification and preventive intervention in high-risk individuals.
