Online registration of neonatal stroke in Shenzhen: protocol for a multicentre, prospective, observational cohort study

Introduction Neonatal stroke remains a primary cause of permanent neurological disability. While global prognostic frameworks, including magnetic resonance imaging (MRI) of the posterior limb of the internal capsule (PLIC), General Movements Assessment (GMA), and the Hammersmith Infant Neurological Examination (HINE), have been established to predict motor outcomes, these tools have not been systematically integrated into large-scale Chinese clinical registries. In regions like Shenzhen, the scarcity of prospective, multicenter data continues to hinder the characterization of stroke incidence, risk factors, and long-term outcomes. This protocol details the establishment of the first dedicated online registry in Shenzhen to address these critical evidence gaps. Methods and analysis This multicenter, prospective, observational cohort study aims to enroll 300 neonates diagnosed with stroke, including arterial ischemic, hemorrhagic, and cerebral venous sinus thrombosis subtypes from a network of 22 hospitals in Shenzhen, China, between January 2025 and December 2030. Data collection will utilize standardized electronic case report forms (eCRFs), initially managed via a uniform digital system ( Supplementary File 1 ), with a planned transition to a secure web-based registry for broader access. Participants will undergo longitudinal follow-up until age five, with neurodevelopmental outcomes assessed using the Bayley Scales of Infant and Toddler Development (4th Ed., Chinese Version), 3T cranial MRI, and video-EEG. To explore the metabolic-inflammatory nexus of neonatal injury, placental pathology will be integrated into the clinical data, and a biobank of stool and breast milk samples will be established. The primary outcome is the registry-based incidence of neonatal stroke. In contrast, secondary outcomes include characterizing clinical subtypes, identifying prenatal/perinatal risk factors, and developing and internally validating a robust prognostic prediction model. Clinical Trial Registration: https://www.chictr.org.cn , identifier ChiCTR2500097104.