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A multivariate prediction model and its application in forecasting acute ischemic stroke: Protocol for a
Dongmei Yang1, Xia Liu1, Hui Lan1
1Department of Clinical Laboratory, Zigong Third People's Hospital, Zigong City, China.
Medicine
|December 23, 2022
Summary
This study develops prediction models to identify patients at high risk for acute ischemic stroke (AIS). Early detection through these models aids in preventing and treating this common, disabling condition.
Area of Science:
- Neurology
- Cardiovascular Medicine
- Biostatistics
Background:
- Acute ischemic stroke (AIS) is a significant global health issue, causing widespread death and disability due to blocked cerebral blood vessels.
- Understanding the underlying mechanisms and identifying high-risk individuals are crucial for effective management.
Purpose of the Study:
- To develop and compare prediction models for acute ischemic stroke (AIS) occurrence.
- To explore the pathogenesis of AIS using clinical data.
- To provide a reference for clinical diagnosis and treatment strategies.
Main Methods:
- A retrospective study involving over 18-year-old AIS patients hospitalized between October 2019 and September 2022.
- Collection of demographic data, coronary artery computed tomographic arteriography (CTA) results, and blood biomarker levels.
- Development of single-indicator and joint prediction models with comparative analysis of receiver operating characteristic curves.
Main Results:
- Established prediction models for AIS using demographic, CTA, and biomarker data.
- Comparative analysis of model performance to identify the most accurate predictive tool.
- Identified key indicators for AIS risk stratification.
Conclusions:
- The developed models offer guidance for the prevention and treatment of AIS.
- Analysis of high-risk groups facilitates targeted interventions.
- Findings encourage further research into AIS pathogenesis and clinical management.
