AUTHOR=Phan Thanh G. , Clissold Benjamin B. , Ma Henry , Ly John Van , Srikanth Velandai 

TITLE=Predicting Disability after Ischemic Stroke Based on Comorbidity Index and Stroke Severity—From the Virtual International Stroke Trials Archive-Acute Collaboration

JOURNAL=Frontiers in Neurology

VOLUME=Volume 8 - 2017

YEAR=2017

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2017.00192

DOI=10.3389/fneur.2017.00192

ISSN=1664-2295

ABSTRACT=Background and Aim: The availability and access of hospital administrative data (coding for Charlson comorbidity index [CCI]) in large data form has resulted in a surge of interest in using this information to predict mortality from stroke.  The aims of this study were to determine the minimum clinical dataset to be included in models for predicting disability after ischemic stroke adjusting for CCI and clinical variables; and to evaluate the impact of CCI on prediction of outcome. 
Method: We leverage anonymized clinical trial data in the Virtual International Stroke Trials Archive (VISTA). This repository contains prospective data on stroke severity and outcome. The inclusion criteria were patients with available stroke severity score such as National Institutes of Health Stroke Scale (NIHSS), imaging data and outcome disability score such as 90 day Rankin Scale.
We calculate CCI based on comorbidity data in this dataset. Statistics for logistic regression models calibration were as follow: Nagelkerke generalised R2 and Brier score;  and discrimination: area under the receiver operating characteristics curve (AUC) and integrated discrimination improvement (IDI). The IDI was used to evaluate improvement in disability prediction above baseline model containing age, sex and CCI. 
Results: The clinical data among 5206 patients (55% males) were: mean age 69±13 years, CCI 4.2±0.8, median NIHSS of 12 (IQR 8, 17) on admission and 9 (IQR 5, 15) at 24 hours. In model 2, adding admission NIHSS to the baseline model improved AUC from 0.67 (95% CI 0.65 - 0.68) to 0.79 (95% CI 0.78 - 0.81). In model 3, adding 24-hour NIHSS to the baseline model resulted in substantial improvement in AUC to 0.90 (95% CI 0.89 - 0.91) and increased IDI by 0.23 (95% CI 0.22-0.24). Adding the variable rTPA did not result in a further change in AUC or IDI to this regression model. In model 3, the variable NIHSS at 24 hour explains 87.3% of the variance of model 3, follow by age (8.5%), comorbidity (3.7%) and male sex (0.5%).
Conclusion: Our results suggest that prediction of disability after ischemic stroke should at least include 24-hour NIHSS and age. The variable CCI is