A Prediction Model for Rapid Identification of Ischemic Stroke: Application of Serum Soluble Corin

Abstract

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.

Keywords:

• ischemic stroke
• serum corin
• biomarker
• prediction model

Abbreviations

AUC, area under the receiver-operating characteristic curve; CI, confidence interval; S100β, glial protein; GFAP, glial fibrillary acidic protein; MMP-9, matrix metalloproteinase-9; UCH-L1, ubiquitin C-terminal hydrolase-L1; NDKA, nucleoside diphosphate kinase A; CRP, C-reactive protein; MBP, myelin basic protein; CT, computed tomography; MRI, magnetic resonance imaging; SBP, systolic blood pressure; DBP, diastolic blood pressure; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; Log-corin, Log-transformed corin; ROC, receiver operating characteristic; CHD, coronary heart disease; DCA, decision curve analysis; IQR, interquartile range.

Acknowledgments

We gratefully acknowledge the cooperation and participation of the members of the Gusu cohort. We especially thank the clinical staff at all participating hospitals for their support and contribution to this project. Without their contribution, this research would not have been possible.

Disclosure

None of the authors have financial associations that might pose a conflict of interest in connection with the submitted article. The datasets used during the current study are available from the corresponding author on a reasonable request.

Additional information

Funding

This study was supported by the National Natural Science Foundation of China (NO. 82173596, 81903384, and 81872690), the Soochow University Undergraduate Extracurricular Academic Research Program (KY20220120A), the Suzhou Municipal Science and Technology Bureau (NO. SYS2020091 and SKJY2021040), and a Project of the Priority Academic Program Development of Jiangsu Higher Education Institutions.