http://journal.frontiersin.org/article/10.3389/fneur.2017.00192/full?
Thanh G. Phan*,
Benjamin B. Clissold,
Henry Ma,
John Van Ly and 
Velandai Srikanth on Behalf of the VISTA-Acute Collaborators
- Stroke Unit, Monash Health and Stroke and Aging Research Group, Monash University, Melbourne, VIC, Australia
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.(Not stopping mortality from stroke!) The aims of this
study were to determine the minimum clinical data set 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. 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 data set. For logistic regression, we
used these calibration statistics: Nagelkerke generalised R2
and Brier score; and for discrimination we used: 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 5,206 patients
(55% males) were as follows: mean age 69 ± 13 years, CCI 4.2 ± 0.8, and
median NIHSS of 12 (IQR 8, 17) on admission and 9 (IQR 5, 15) at 24 h.
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-h 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 recombinant tissue plasminogen
activator did not result in a further change in AUC or IDI to this
regression model. In Model 3, the variable NIHSS at 24 h 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-h NIHSS and
age. The variable CCI is less important for prediction of disability in
this data set.
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