Development of a Clinical Prediction Model for Ultra-Early Mild Acute Ischemic Stroke: A Comprehensive Review and Future Directions
Introduction
Cerebrovascular disease, particularly acute ischemic stroke (AIS) caused by cerebral atherosclerosis, remains a significant contributor to illness and death in China. AIS is characterized by a localized interruption of blood flow to the brain, leading to various neurological impairments. Among stroke subtypes, AIS is the most prevalent and is associated with high rates of disability and mortality.1,2 Intravenous thrombolysis administered within 6 hours of symptom onset has been shown to significantly improve neurological outcomes in AIS patients.3–5 However, the narrow therapeutic window necessitates rapid and accurate diagnosis.6 Transient ischemic attack (TIA), often a precursor to ischemic stroke, shares similar pathological mechanisms and is managed with antiplatelet agents, antithrombotic therapy, and measures to enhance cerebral perfusion.7
Current clinical guidelines acknowledge the difficulty in distinguishing CT-negative ultra-early mild AIS from TIA based solely on clinical presentation. Magnetic resonance imaging with diffusion-weighted imaging (MRI-DWI) is the gold standard for differentiation but is frequently inaccessible in primary hospitals due to high costs and time constraints.8 Consequently, computed tomography (CT) is widely used for initial assessment. However, CT has limited sensitivity in detecting early ischemic changes, with false-negative rates as high as 32% in mild AIS cases,9 potentially leading to delays in thrombolytic therapy.
In recent years, serum biomarkers have gained attention for their roles in the pathogenesis and prognosis of ischemic stroke. Inflammation, endothelial dysfunction, and metabolic alterations play critical roles in acute cerebral ischemia.10 Markers such as high-sensitivity C-reactive protein (hs-CRP), homocysteine (HCY), and lipid profiles have been associated with stroke risk and outcomes.11,12 Moreover, dynamic changes in neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) may reflect the acute inflammatory state following cerebral ischemia.13 Although these markers show promise, their combined utility in differentiating CT-negative mild AIS from TIA in the hyperacute phase (<6 hours) remains underexplored.
Model Development and Validation
This study aimed to create and validate a clinical prediction model that integrates NIHSS scores with readily available serum biomarkers to help distinguish between CT-negative mild AIS and TIA at an early stage. The goal is to offer a practical and quick diagnostic tool that can aid clinical decision-making in situations where MRI is not accessible. By doing so, the model aspires to enhance timely interventions and ultimately improve patient outcomes.
The study included 330 patients, comprising 205 with AIS and 125 with TIA, admitted to a comprehensive hospital in Shishi City, China, between January 2020 and December 2023. The final diagnosis of AIS and TIA depended on MRI-DWI. The model was developed using multivariate logistic regression, incorporating NIHSS scores, CRP, GLU, TCHO, TG, and LDL. The model demonstrated strong discriminative ability, good calibration, and clinical utility across both training and validation cohorts.
Future Directions
The study highlights the need for further research to validate and refine the model in multi-center, prospective cohorts. Incorporating advanced biomarkers or neuroimaging features could further improve predictive accuracy. The potential of artificial intelligence-based integration of clinical, laboratory, and imaging data also warrants exploration.
Conclusion
This prediction model provides a practical, evidence-based tool for identifying CT-negative ultra-early mild AIS in resource-limited settings. Its integration into clinical workflows may accelerate thrombolysis initiation, reduce diagnostic delays, and improve functional recovery. Future studies should validate the model and assess its impact on real-world clinical decision-making and healthcare resource utilization.
