Development and Validation of a Model Based on Circulating Biomarkers for Discriminating Symptomatic Spontaneous Intracranial Artery Dissection.

Abstract

Spontaneous intracranial artery dissection (sIAD) is the leading cause of stroke in young individuals. Identifying high-risk sIAD cases that exhibit symptoms and are likely to progress is crucial for treatment decision-making. This study aimed to develop a model relying on circulating biomarkers to discriminate symptomatic sIADs. The study prospectively collected sIAD tissues and corresponding serums from January 2020 to December 2022 as the discovery cohort. Symptomatic sIADs were defined as those with mass effect, hemorrhagic, or ischemic stroke. A stratification model was developed using the machine-learning algorithm within the derivation cohort (a cross-sectional cohort including from January 2018 to August 2022) and validated within the validation cohort (a longitudinal cohort including from January 2017 to April 2023). In the discovery cohort (n = 10, 5 symptomatic), analyses of tissues and serums revealed 15 proteins and 2 cytokines with significance between symptomatic and asymptomatic sIADs. Among these biomarkers, six proteins and one cytokine, participating in the immune response and inflammatory-related pathways, have a good consistency in expression level between sIAD tissues and serums. In the derivation cohort (n = 181, 77 symptomatic), a model incorporating these 7 biomarkers was highly discriminative of symptomatic sIADs (area under curve [AUC], 0.95). This model performed well in predicting the occurrence of sIAD-related symptoms in the validation cohort (n = 84, 26 symptomatic) with an AUC of 0.88. This study revealed seven circulating biomarkers of symptomatic sIAD and provided a high-accuracy model relying on these circulating biomarkers to identify symptomatic sIADs, which may aid in clinical decision-making for sIADs.