Development of a Clinical Prediction Model for Ultra-Early Mild Acute Ischemic Stroke: A Comprehensive Analysis and Discussion
Introduction:
Cerebrovascular disease, particularly Acute Ischemic Stroke (AIS), remains a significant health concern in China, emphasizing the need for accurate and timely diagnosis. This study aims to develop a clinical prediction model for AIS, focusing on CT-negative ultra-early mild cases, to enhance diagnostic accuracy and improve patient outcomes.
Methodology:
The research involved a retrospective analysis of patients with CT-negative ultra-early mild AIS and TIA admitted to a comprehensive hospital in Shishi City, China, between 2020 and 2023. The study included 330 patients, with 205 AIS and 125 TIA cases. The inclusion criteria emphasized the critical 6-hour window post-symptom onset, CT-negative results, and specific scoring thresholds for mild AIS. The exclusion criteria were designed to ensure a focused and controlled study.
Results:
The study revealed that the NIHSS score, CRP, random blood glucose, total cholesterol, triglycerides, and LDL were independent predictors of CT-negative mild AIS. The NIHSS score emerged as the strongest predictor, highlighting its role in quantifying early neurological deficits. The model demonstrated strong discriminative ability, good calibration, and clinical utility, with AUC values of 0.830 in the training set and 0.804 in the validation set.
Discussion:
This study contributes to the understanding of AIS diagnosis by emphasizing the importance of lipid and metabolic markers, along with inflammatory markers like CRP. The model's strong performance and reliance on commonly available parameters make it a valuable tool for resource-limited settings. However, the single-center nature and small sample size limit generalizability, calling for further multi-center studies to validate and refine the model.
Conclusion:
In conclusion, the developed clinical prediction model offers a practical approach to identifying CT-negative ultra-early mild AIS, potentially improving thrombolysis initiation and patient outcomes. Future research should focus on validating the model in diverse populations and assessing its impact on clinical decision-making and healthcare resource utilization.
