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A Prediction Model for Rapid Identification of Ischemic Stroke: Application of Serum Soluble Corin
Ying Lu1, Weiqi Wang1, Zijie Tang1
1Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, People's Republic of China.
Insights
Rapid identification of ischemic stroke is crucial. A new diagnostic model using routine variables like age, blood pressure, and serum corin aids in quick diagnosis, showing high accuracy in validation studies.
Area of Science:
- Neurology
- Biomarkers
- Diagnostic Tools
Background:
- Timely diagnosis of ischemic stroke is critical due to a narrow therapeutic window.
- Existing diagnostic methods may not always facilitate rapid identification.
Purpose of the Study:
- To develop and validate a diagnostic model for the rapid identification of ischemic stroke.
Main Methods:
- A logistic regression model was developed using a training sample (n=1547) and validated on a testing sample (n=1548).
- Predictors included demographic data, risk factors (smoking, hypertension), clinical measurements (blood pressure, lipids, glucose), and serum corin levels.
- Serum corin was measured using ELISA kits.
Main Results:
- The final model incorporated age, sex, smoking, family history of stroke, hypertension history, systolic blood pressure, total cholesterol, HDL cholesterol, fasting glucose, and serum corin.
- The model demonstrated strong discrimination in both training (AUC: 0.910) and testing (AUC: 0.907) samples.
- Good calibration was observed, indicating reliable prediction of ischemic stroke probability.
Conclusions:
- A simple diagnostic model using readily available variables was successfully developed for rapid ischemic stroke identification.
- The model shows potential for clinical utility, warranting further investigation into its effectiveness and efficiency.
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 P>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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