Developing and validating a predictive model for stroke progression

Craig, L.E., Wu, O. , Gilmour, H., Barber, M. and Langhorne, P. (2011) Developing and validating a predictive model for stroke progression. Cerebrovascular Diseases, 1(1), pp. 105-114. (doi: 10.1159/000334473)

Abstract

<p><b>Background:</b> Progression is believed to be a common and important complication in acute stroke, and has been associated with increased mortality and morbidity. Reliable identification of predictors of early neurological deterioration could potentially benefit routine clinical care. The aim of this study was to identify predictors of early stroke progression using two independent patient cohorts.</p> <p><b>Methods:</b> Two patient cohorts were used for this study – the first cohort formed the training data set, which included consecutive patients admitted to an urban teaching hospital between 2000 and 2002, and the second cohort formed the test data set, which included patients admitted to the same hospital between 2003 and 2004. A standard definition of stroke progression was used. The first cohort (n = 863) was used to develop the model. Variables that were statistically significant (p < 0.1) on univariate analysis were included in the multivariate model. Logistic regression was the technique employed using backward stepwise regression to drop the least significant variables (p > 0.1) in turn. The second cohort (n = 216) was used to test the performance of the model. The performance of the predictive model was assessed in terms of both calibration and discrimination. Multiple imputation methods were used for dealing with the missing values.</p> <p><b>Results:</b> Variables shown to be significant predictors of stroke progression were conscious level, history of coronary heart disease, presence of hyperosmolarity, CT lesion, living alone on admission, Oxfordshire Community Stroke Project classification, presence of pyrexia and smoking status. The model appears to have reasonable discriminative properties [the median receiver-operating characteristic curve value was 0.72 (range 0.72–0.73)] and to fit well with the observed data, which is indicated by the high goodness-of-fit p value [the median p value from the Hosmer-Lemeshow test was 0.90 (range 0.50–0.92)].</p> <p><b>Conclusion:</b> The predictive model developed in this study contains variables that can be easily collected in practice therefore increasing its usability in clinical practice. Using this analysis approach, the discrimination and calibration of the predictive model appear sufficiently high to provide accurate predictions. This study also offers some discussion around the validation of predictive models for wider use in clinical practice.</p>

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Langhorne, Professor Peter and Craig, Dr Louise and Wu, Professor Olivia
• Authors: Craig, L.E., Wu, O., Gilmour, H., Barber, M., and Langhorne, P.
• College/School: College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > Health Economics and Health Technology Assessment; College of Medical Veterinary and Life Sciences > School of Cardiovascular & Metabolic Health
• Journal Name: Cerebrovascular Diseases
• Publisher: Karger
• ISSN: 1015-9770
• ISSN (Online): 1421-9786
• Published Online: 03 December 2011
• Copyright Holders: Copyright © 2011 Karger
• First Published: First published in Cerebrovascular Diseases 1(1):105-114
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher