A multivariable prediction model for ischaemic stroke recurrence was developed and validated in Chinese patients with symptomatic intracranial atherosclerotic stenosis.
Can a multivariable prediction model predict ischaemic stroke recurrence in patients with symptomatic intracranial atherosclerotic stenosis?
Patients with symptomatic intracranial atherosclerotic stenosis in China
Multivariable prediction model
Ischaemic stroke recurrencehard clinical
The PROMISE study developed and validated a multivariable prediction model for ischaemic stroke recurrence in Chinese patients with symptomatic intracranial atherosclerotic stenosis.
BACKGROUND: Intracranial atherosclerotic stenosis is a prevalent cause of stroke worldwide and carries a high risk of recurrence. This study aimed to develop and validate a simple and effective model for predicting individualised risks of recurrent ischaemic stroke in patients with symptomatic intracranial atherosclerotic stenosis (ICAS). METHODS: This multivariable prediction model was built and validated using participants with symptomatic ICAS within 30 days of symptom onset from a large randomised controlled trial (RICA). The trial enrolled 3033 participants across 84 hospitals in China between Oct 28, 2015, and Feb 28, 2019. Participants were non-randomly divided by hospitals location into training and validation sets. Eligible participants were aged 40-80 years and had experienced an ischaemic stroke or transient ischaemic attack attributable to 50-99% stenosis of a major intracranial artery. The primary outcome of the model was time to first ischaemic stroke recurrence within 1 year. A Cox proportional hazards model was developed using the Akaike information criterion and validated both internally and externally. Model performance was assessed by discrimination (Harrell's concordance index C-index), calibration (modified Hosmer-Lemeshow test and calibration plots), and clinical utility (decision curve analysis and Kaplan-Meier curves). FINDINGS: 2995 participants from the RICA trial were divided into the training (n=2137) and validation (n=858) sets. The PROMISE model included age, BMI, hypertension, type 1 and type 2 diabetes, current smoking status, LDL cholesterol, location of symptomatic stenosis, and stenosis degree of qualifying artery as predictors. The C-index for the prediction model was 0·81 (95% CI 0·80-0·83) in the training set and 0·78 (0·77-0·84) in the validation set. Model calibration was satisfactory across the full risk profile (modified Hosmer-Lemeshow test χ2=5·32, p=0·38). Furthermore, decision curve analysis curves indicated that this prediction model provided clinical benefits. Additionally, participants were classified into three distinct risk groups (low, medium, and high) by Kaplan-Meier curves based on the prediction model. The corresponding C-indices for these groups were 0·80 (95% CI 0·80-0·86), 0·68 (0·67-0·77), and 0·71 (0·69-0·81) in the training set, and 0·76 (0·75-0·88), 0·67 (0·65-0·82), and 0·68 (0·67-0·86) in the validation set. Based on the final model, an online risk calculator was developed. INTERPRETATION: We developed the PROMISE model and an online calculator using accessible clinical variables to predict ischaemic stroke recurrence, identify individuals at high risk, and support symptomatic ICAS patient management. The model had a strong discriminative ability and good calibration. Further validation and model optimisation should be conducted to support stroke care. FUNDING: National Natural Science Foundation of China, Beijing Natural Science Foundation, Noncommunicable Chronic Diseases-National Science and Technology Major Project, and Beijing Physician Scientist Training Project.
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Chengbei Hou
Xiao Dong
Yuanyuan Liu
The Lancet Digital Health
Capital Medical University
Chinese Institute for Brain Research
Xuan Wu Hospital of the Capital Medical University
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Hou et al. (Wed,) reported a other. A multivariable prediction model for ischaemic stroke recurrence was developed and validated in Chinese patients with symptomatic intracranial atherosclerotic stenosis.
www.synapsesocial.com/papers/69ec593e88ba6daa22dab2ca — DOI: https://doi.org/10.1016/j.landig.2026.100989