Development of a Clinical Prediction Model for Ultra-Early Mild Acute Ischemic Stroke: A Novel Approach to Early Diagnosis and Treatment
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. AIS is the most prevalent stroke subtype 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 AIS, shares similar pathological mechanisms and is managed with antiplatelet agents and antithrombotic therapy.
Current clinical guidelines face challenges in distinguishing CT-negative ultra-early mild AIS from TIA based solely on clinical presentation. MRI 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.
Biomarkers and Model Development:
Recent studies have highlighted the potential of serum biomarkers in improving early diagnostic accuracy for AIS. Inflammation, endothelial dysfunction, and metabolic alterations play critical roles in acute cerebral ischemia. Markers such as high-sensitivity C-reactive protein (hs-CRP), homocysteine (HCY), and lipid profiles have been associated with stroke risk and outcomes. Dynamic changes in neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) may reflect the acute inflammatory state following cerebral ischemia. While these markers show promise, their combined utility in differentiating CT-negative mild AIS from TIA in the hyperacute phase remains underexplored.
The 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 for clinical decision-making in MRI-unavailable settings, enhancing timely interventions and patient outcomes.
Methodology and Results:
The study included patients with CT-negative ultra-early mild AIS and TIA admitted to a comprehensive hospital in Shishi City, China, between 2020 and 2023. Mild AIS was defined by specific scoring thresholds (NIHSS ≤ 5), while TIA was characterized by temporary blood flow disruption causing symptoms lasting less than 24 hours with minimal lasting neurological damage. The hospital's high-quality medical services and well-organized resources made it representative for the region.
The study's sample size was determined using the empirical rule for multivariate regression models, considering the number of variables. After literature review, theoretical analysis, and pre-test screening, 6 independent variables were finalized. The estimated sample size range was 60 to 120 cases, and the planned sample size was set at 330 cases, meeting statistical requirements.
Laboratory measurements included complete blood count (CBC), C-reactive protein (CRP), D-dimer, lipid profiles, and glucose levels. Variables such as gender, age, hypertension history, diabetes history, NIHSS score, HCY, neutrophil count, lymphocyte count, monocyte count, platelet count, CRP, fibrinogen, D-dimer, GLU, TG, HDL, LDL, NLR, and PLR were collected.
Multivariate logistic regression identified independent risk factors, and a clinical prediction model was developed using R software. The model was evaluated for discrimination, calibration, and clinical utility. A nomogram was constructed to visualize the model, allowing for the calculation of AIS risk based on predictor scores.
The model demonstrated strong discriminative ability with AUC values of 0.830 in the training set and 0.804 in the validation set. Calibration and decision curve analysis further confirmed the model's clinical relevance. The model's advantage lies in its reliance on commonly available clinical and laboratory parameters, making it suitable for resource-limited environments.
Discussion and Future Directions:
The study's findings highlight the model's potential to facilitate early diagnosis of CT-negative mild AIS, especially in MRI-unavailable settings. By enabling faster treatment decisions, the model can minimize delays in thrombolysis, leading to better functional outcomes and reduced disability. However, limitations include a single-center study with a small sample size, which may limit generalizability.
Future studies should validate the model in multi-center, prospective cohorts and assess its impact on real-world clinical decision-making and healthcare resource utilization. Incorporating advanced biomarkers or neuroimaging features could further improve predictive accuracy, and the potential of artificial intelligence integration warrants exploration.
Conclusion:
The 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. Further research is needed to validate and refine the model, ensuring its impact on patient outcomes.
