Development of a Clinical Prediction Model for Ultra-Early Mild Acute Ischemic Stroke: A Comprehensive Study
Introduction:
Cerebrovascular disease, particularly Acute Ischemic Stroke (AIS) caused by cerebral atherosclerosis, remains a significant health concern in China. AIS, characterized by localized blood flow interruption to the brain, leads to various neurological impairments. Among stroke subtypes, AIS is the most prevalent and is associated with high disability and mortality rates. Intravenous thrombolysis within 6 hours of symptom onset has shown to improve neurological outcomes in AIS patients. However, the narrow therapeutic window necessitates rapid and accurate diagnosis. Transient ischemic attack (TIA), often a precursor to ischemic stroke, shares similar pathological mechanisms and is managed with antiplatelet agents and antithrombotic therapy.
Current guidelines acknowledge the challenge 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 often inaccessible in primary hospitals due to high costs and time constraints. Computed tomography (CT) is widely used for initial assessment, but its sensitivity in detecting early ischemic changes is limited, leading to potential delays in thrombolytic therapy.
Methodology:
This retrospective study focused on patients with CT-negative ultra-early mild AIS and TIA admitted to a comprehensive hospital in Shishi City, China, between January 2020 and December 2023. Mild AIS was defined by mild neurological deficits with specific scoring thresholds (NIHSS ≤ 5), while TIA is characterized by a temporary blood flow disruption causing symptoms lasting less than 24 hours with minimal or no lasting neurological damage. The hospital's high-quality medical services and well-organized resources made it an ideal setting for diagnosing and treating patients. A total of 330 patients were included, comprising 205 AIS and 125 TIA cases. The final diagnosis relied on MRI-DWI.
Results:
Baseline characteristics were compared between AIS and TIA subgroups. Several variables differed significantly, with AIS showing higher NIHSS scores, elevated homocysteine levels, increased platelet counts, higher C-reactive protein levels, and higher random blood glucose levels. Multivariate logistic regression identified NIHSS score, CRP, random blood glucose, total cholesterol, triglycerides, and LDL as independent risk factors for CT-negative mild AIS. The model demonstrated strong discriminative ability with AUC values of 0.830 in the training set and 0.804 in the validation set.
Discussion:
This study developed and validated a clinical prediction model for CT-negative ultra-early mild AIS, incorporating NIHSS score, CRP, glucose, total cholesterol, triglycerides, and LDL. The model showed robust performance and clinical utility, offering a practical tool for early diagnosis in resource-limited settings. It has the potential to enhance timely interventions and improve patient outcomes.
Limitations and Future Directions:
The study had limitations, including a single-center design and a small sample size. Future multi-center studies with larger cohorts are needed to validate and refine the model. Incorporating advanced biomarkers and neuroimaging features could further improve predictive accuracy, and exploring artificial intelligence integration is promising.
Conclusion:
The prediction model provides a practical, evidence-based tool for identifying CT-negative ultra-early mild AIS in resource-limited settings. It has the potential to accelerate thrombolysis initiation, reduce diagnostic delays, and improve functional recovery. Future research should validate the model in diverse populations and assess its impact on clinical decision-making and healthcare resource utilization.
