Frontiers in neurology · 2026
Clinical and high-resolution magnetic resonance imaging-based prediction of ischemic stroke in cervical artery dissection.
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Zhang Xuanxiao, Liu Chunmei, Yin Shuo, Tian Xueliang, Li Tao, Lan Wenjing, Li Hai, Zhou Hongwei
Аннотация
BACKGROUND: Cervical artery dissection (CeAD) is an important cause of ischemic stroke, yet early risk stratification remains challenging. This study aimed to identify clinical and high-resolution vessel wall magnetic resonance imaging features associated with ischemic stroke and to develop a patient-level model for short-term risk prediction.
METHODS: A total of 129 patients with CeAD (148 dissected vessels) were retrospectively included. Baseline clinical data and HRMRI features were collected. At the vessel level, least absolute shrinkage and selection operator (LASSO) regression was used for variable selection, followed by a mixed-effects logistic regression model to identify factors associated with ischemic events. At the patient level, representative vascular imaging features were integrated with clinical variables. LASSO regression and multivariable logistic regression were applied to construct a nomogram for risk prediction. Model performance was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis.
RESULTS: At the vessel level, white blood cell (WBC) count, intraluminal thrombus, severe stenosis or occlusion, and alcohol consumption were independently associated with ischemic events. At the patient level, multivariable analysis showed that WBC count, intraluminal thrombus, male sex, and alcohol consumption were independent predictors of ischemic stroke. The nomogram exhibited good discriminative ability, with an optimism-corrected area under the curve of 0.837 (95% CI: 0.810-0.852), along with satisfactory calibration and clinical net benefit.
CONCLUSION: A patient-level model shows good performance in predicting ischemic stroke risk in patients with CeAD and may assist in early risk stratification and individualized clinical decision-making.
METHODS: A total of 129 patients with CeAD (148 dissected vessels) were retrospectively included. Baseline clinical data and HRMRI features were collected. At the vessel level, least absolute shrinkage and selection operator (LASSO) regression was used for variable selection, followed by a mixed-effects logistic regression model to identify factors associated with ischemic events. At the patient level, representative vascular imaging features were integrated with clinical variables. LASSO regression and multivariable logistic regression were applied to construct a nomogram for risk prediction. Model performance was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis.
RESULTS: At the vessel level, white blood cell (WBC) count, intraluminal thrombus, severe stenosis or occlusion, and alcohol consumption were independently associated with ischemic events. At the patient level, multivariable analysis showed that WBC count, intraluminal thrombus, male sex, and alcohol consumption were independent predictors of ischemic stroke. The nomogram exhibited good discriminative ability, with an optimism-corrected area under the curve of 0.837 (95% CI: 0.810-0.852), along with satisfactory calibration and clinical net benefit.
CONCLUSION: A patient-level model shows good performance in predicting ischemic stroke risk in patients with CeAD and may assist in early risk stratification and individualized clinical decision-making.