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Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke
Published on: February 22, 2020
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Web-based tool for dynamic functional outcome after acute ischemic stroke and comparison with existing models.
Ruijun Ji1,2, Wanliang Du3,4, Haipeng Shen5
1Tiantan Comprehensive Stroke Center, Beijing Tiantan Hospital, Capital Medical University, No. 6 Tiantanxili, Beijing, 100050, Dongcheng District, China. JRJChina@sina.com.
BMC Neurology
|May 1, 2015
Summary
A new web-based tool, Dynamic Functional Status after Acute Ischemic Stroke (DFS-AIS), accurately predicts patient recovery after stroke. This model offers improved prediction compared to existing tools, aiding in better patient management and outcomes.
Area of Science:
- Neurology
- Medical Informatics
- Public Health
Background:
- Acute ischemic stroke (AIS) is a major global cause of death and disability.
- Predicting functional recovery is crucial for patient management after AIS.
Purpose of the Study:
- To develop and validate a web-based risk model, DFS-AIS, for predicting dynamic functional status at multiple time points post-AIS.
- To assess the model's performance against existing prediction tools.
Main Methods:
- Development of the DFS-AIS model using data from the China National Stroke Registry (CNSR) with 12,026 patients.
- Random division into derivation (60%) and validation (40%) cohorts.
- Multivariable logistic regression identified predictors; model performance assessed by AUROC and calibration plots.
Main Results:
- The DFS-AIS demonstrated good discrimination (AUROC 0.837-0.845) and excellent calibration (r=0.99) in both cohorts.
- Identified predictors include age, gender, comorbidities (diabetes, prior stroke/TIA, smoking, atrial fibrillation), pre-stroke dependence, statin use, NIHSS score, and blood glucose.
- DFS-AIS outperformed 8 existing models in predicting functional outcome and mortality.
Conclusions:
- The DFS-AIS is a validated and robust risk model for predicting functional outcomes at various time points after AIS.
- This tool can aid clinicians in anticipating patient recovery trajectories and optimizing care.
