Objective: Rapid identification is critical for ischemic stroke due to the very narrow therapeutic time window. The objective of this study was to construct a diagnostic model for the rapid identification of ischemic stroke.
Methods: A mixture population constituted of patients with ischemic stroke (n = 481), patients with hemorrhagic stroke (n = 116), and healthy individuals from communities (n = 2498) were randomly resampled into training (n = 1547, mean age: 55 years, 44% males) and testing (n = 1548, mean age: 54 years, 43% males) samples. Serum corin was assayed using commercial ELISA kits. Potential risk factors including age, sex, education level, cigarette smoking, alcohol consumption, obesity, blood pressure, lipids, glucose, and medical history were obtained as candidate predictors. The diagnostic model of ischemic stroke was developed using a backward stepwise logistic regression model in the training sample and validated in the testing sample.
Results: The final diagnostic model included age, sex, cigarette smoking, family history of stroke, history of hypertension, systolic blood pressure, total cholesterol, high-density lipoprotein cholesterol, fasting glucose, and serum corin. The diagnostic model exhibited good discrimination in both training (AUC: 0.910, 95% CI: 0.884-0.936) and testing (AUC: 0.907, 95% CI: 0.881-0.934) samples. Calibration curves showed good concordance between the observed and predicted probability of ischemic stroke in both samples (all >0.05).
Conclusion: We developed a simple diagnostic model with routinely available variables to assist rapid identification of ischemic stroke. The effectiveness and efficiency of this model warranted further investigation.
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