Development of a Clinical Prediction Model for Ultra-Early Mild Acute Ischemic Stroke with Negative CT Results

Clinical guidelines highlight the challenge of distinguishing CT-negative ultra-early mild acute ischemic stroke (AIS) from transient ischemic attack (TIA) based solely on clinical manifestations. MRI-DWI, the gold standard for differentiation, is often inaccessible in primary hospitals due to cost and time constraints, leading to widespread CT use-with a false-negative rate in mild AIS, risking delayed thrombolysis.This study aims to establish a clinical prediction model for CT-negative ultra-early mild acute ischemic stroke (AIS) by comparing the clinical data and laboratory test results of patients with CT-negative early mild ischemic stroke and transient ischemic attack (TIA), in order to promote early diagnosis and early treatment of AIS, thereby improving patient prognosis.

This study compared and analyzed the clinical data of gender, age, history of hypertension, history of diabetes, National Institutes of Health Stroke Scale (NIHSS) score, and laboratory test results such as homocysteine, neutrophil count, lymphocyte count, monocyte count, platelet count (PLT), C-reactive protein (CRP), fibrinogen (FIG), D-dimer, Random Blood glucose (GIU), total cholesterol (TCHO), triglycerides (TG), high density lipoprotein cholesterol (HDL), low density lipoprotein cholesterol (LDL), neutrophil count, lymphocyte count, and platelet count to calculate the neutrophil to lymphocyte ratio(NLR) and platelet to lymphocyte ratio (PLR) in CT-negative ultra-early mild AIS and TIA patients, variables showing significant differences in difference analysis were included in a multivariate logistic regression model to identify independent predictors of AIS,and the independent predictors were used to established a clinical prediction model for the diagnosis of early AIS with negative CT based on the statistical results.

Multivariate analysis revealed that the NIHSS score, C-reactive protein (CRP), random blood glucose (GLU), total cholesterol (TCHO), triglycerides (TG), and low-density lipoprotein cholesterol (LDL-C) are independent predictors of computed tomography (CT)-negative mild acute ischemic stroke (AIS), with odds ratios (ORs) and 95% confidence intervals (CIs) as follows: NIHSS score (OR = 5.497, 95% CI: 3.599-8.395; p < 0.001), CRP (OR = 1.128, 95% CI: 1.005-1.295; p = 0.038), random blood glucose (OR = 1.103, 95% CI: 1.013-1.268; p = 0.043), total cholesterol (OR = 1.626, 95% CI: 1.022-2.931; p = 0.023), triglycerides (OR = 1.337, 95% CI: 1.025-1.721; p = 0.031), and LDL-C (OR = 1.542, 95% CI: 1.142-1.959; p = 0.025). A clinical prediction model that included these factors showed a strong ability to discriminate between outcomes, achieving area under the curve (AUC) values of 0.830 in the training set and 0.804 in the validation set. Additionally, the model demonstrated good calibration and clinical utility across various threshold probabilities, indicating its effectiveness in practical applications.

Our study demonstrated that The developed clinical prediction model can assist in diagnosing CT-negative ultra-early mild AIS, thereby potentially improving patient outcomes through timely intervention.