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Updated: May 22, 2026

Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke
09:10

Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke

Published on: February 22, 2020

Developing and validating a predictive model for stroke progression.

L E Craig1, O Wu, H Gilmour

  • 1College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK.

Cerebrovascular Diseases Extra
|May 9, 2012
PubMed
Summary
This summary is machine-generated.

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Predicting early stroke progression is crucial for patient outcomes. This study identified key clinical factors, including conscious level and medical history, to forecast stroke progression in acute stroke patients.

Area of Science:

  • Neurology
  • Clinical Medicine
  • Biostatistics

Background:

  • Stroke progression is a significant complication associated with increased mortality and morbidity.
  • Early identification of predictors for neurological deterioration can improve clinical care.
  • This study aimed to identify predictors of early stroke progression.

Purpose of the Study:

  • To develop and validate a predictive model for early stroke progression.
  • To identify key clinical variables associated with stroke progression.
  • To assess the model's performance in independent patient cohorts.

Main Methods:

  • Utilized two independent patient cohorts (n=863 training, n=216 testing).
  • Employed logistic regression with backward stepwise elimination for model development.
Keywords:
Acute stroke carePredictors of outcomeStroke outcome

Related Experiment Videos

Last Updated: May 22, 2026

Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke
09:10

Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke

Published on: February 22, 2020

  • Assessed model performance using calibration and discrimination metrics, with multiple imputation for missing data.

    • Main Results:

    • Significant predictors included conscious level, coronary heart disease history, hyperosmolarity, CT lesion, living alone, Oxfordshire Community Stroke Project classification, pyrexia, and smoking status.
    • The predictive model demonstrated reasonable discriminative properties (median ROC 0.72).
    • The model showed good fit with observed data (median Hosmer-Lemeshow p=0.90).

    Conclusions:

    • The developed predictive model incorporates easily collectable variables, enhancing clinical usability.
    • The model's discrimination and calibration are sufficiently high for accurate predictions.
    • This study discusses the validation of predictive models for broader clinical application.