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), remains a significant health concern in China, with AIS being the most prevalent stroke subtype. Timely diagnosis and treatment are crucial for improving patient outcomes, especially with the narrow therapeutic window of intravenous thrombolysis. This study aims to develop a clinical prediction model integrating NIHSS scores and serum biomarkers to distinguish CT-negative mild AIS from TIA in the ultra-early phase.
Methodology and Results:
The study included patients with CT-negative ultra-early mild AIS and TIA from a comprehensive hospital in Shishi City, China. The model incorporated NIHSS scores, serum biomarkers (CRP, GLU, TCHO, TG, LDL), and other clinical parameters. Multivariate logistic regression identified NIHSS, CRP, glucose, total cholesterol, triglycerides, and LDL as independent predictors. 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 supported its clinical utility.
Discussion and Future Directions:
This model offers a practical tool for early diagnosis in resource-limited settings, aiding in thrombolysis initiation and functional recovery. However, limitations include a single-center study design and a modest sample size. Future studies should focus on multi-center validation, incorporating emerging biomarkers, and assessing real-world clinical impact.
Conclusion:
The prediction model provides a valuable resource for identifying CT-negative ultra-early mild AIS, potentially improving patient outcomes through timely interventions. Further research is needed to validate and refine the model's clinical application.
