Sim, JW, Teece, L, Dennis, MS and Roffe, C (2016) Validation and Recalibration of Two Multivariable Prognostic Models for Survival and Independence in Acute Stroke. PLoS One, 11 (5). e153527. ISSN 1932-6203

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Abstract

Introduction
Various prognostic models have been developed for acute stroke, including one based on age and five binary variables (‘six simple variables’ model; SSVMod) and one based on age plus scores on the National Institutes of Health Stroke Scale (NIHSSMod). The aims of this study were to externally validate and recalibrate these models, and to compare their predictive ability in relation to both survival and independence.

Methods
Data from a large clinical trial of oxygen therapy (n = 8003) were used to determine the discrimination and calibration of the models, using C-statistics, calibration plots, and Hosmer-Lemeshow statistics. Methods of recalibration in the large and logistic recalibration were used to update the models.

Results
For discrimination, both models functioned better for survival (C-statistics between .802 and .837) than for independence (C-statistics between .725 and .735). Both models showed slight shortcomings with regard to calibration, over-predicting survival and under-predicting independence; the NIHSSMod performed slightly better than the SSVMod. For the most part, there were only minor differences between ischaemic and haemorrhagic strokes. Logistic recalibration successfully updated the models for a clinical trial population.

Conclusions
Both prognostic models performed well overall in a clinical trial population. The choice between them is probably better based on clinical and practical considerations than on statistical considerations.

Item Type: Article
Additional Information: This is the final published version of the article (version of record). It first appeared online via Public Library of Science at http://dx.doi.org/10.1371/journal.pone.0153527 - please refer to any applicable terms of use of the publisher.
Uncontrolled Keywords: prognostic models, stroke
Subjects: R Medicine > R Medicine (General)
Divisions: Faculty of Medicine and Health Sciences > Institute for Science and Technology in Medicine
Depositing User: Symplectic
Date Deposited: 30 May 2016 09:35
Last Modified: 23 Apr 2018 14:04
URI: http://eprints.keele.ac.uk/id/eprint/1790

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