To develop a nomogram to predict risk of recurrent stroke in patients with symptomatic intracranial atherosclerotic stenosis (sICAS), based on conventional risk factors and hemodynamic metrics quantified by computational fluid dynamics (CFD) models.

sICAS is associated with a high risk of recurrent stroke despite optimal medical treatment. Hemodynamic metrics obtained with computed tomographic angiography (CTA)-based CFD models were associated with stroke recurrence in sICAS in our previous study.

Patients with 50-99% sICAS confirmed by CTA were enrolled. Translesional pressure ratio and wall shear stress ratio across sICAS were quantified using CTA-based CFD modeling, to reflect the hemodynamic significance of the lesion. All patients received optimal medical treatment, and the primary outcome was recurrent ischemic stroke in the same territory (SIT) within 1 year. We developed risk prediction nomograms based on multivariate logistic regression, with established vascular risk factors, hemodynamic metrics and other variables identified through univariate analyses. We assessed the discrimination, calibration, risk reclassification and clinical utility of the nomograms. Sensitivity analysis was conducted in patients with anterior-circulation sICAS.

Among 245 sICAS patients, 20 (8.2%) had SIT. A risk prediction nomogram incorporating age, history of hypertension, diabetes, dyslipidemia and hemodynamic status of sICAS lesion, showed good calibration (P for Hosmer-Lemeshow test 0.560) and discrimination (C-statistic 0.73, 95% CI 0.60-0.85). Sensitivity analysis in patients with sICAS in the anterior circulation showed similar results. This nomogram substantially improved risk reclassification of the primary outcome, than nomograms with conventional risk factors only or with severity of sICAS additionally, with significant net reclassification improvement and integrated discrimination improvement, and more net benefits in decision curve analysis.

The nomogram based on conventional vascular risk factors and hemodynamic metrics could be a useful tool to identify sICAS patients at a high risk of recurrent stroke despite optimal medical treatment.