https://www.frontiersin.org/articles/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. 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.
Introduction
Stroke is a leading cause of disability worldwide and results in significant economic and societal cost. Data from Global Burden of Disease 2015 show that stroke and ischemic heart disease accounted for 15.2 million deaths worldwide or approximately 85.1% (84.7–85.5) of all deaths due to cardiovascular disease (1). Hospital administrators across the world (2), international consortium (3), the media, and the publics (4) (http://www.abc.net.au/news/2013-12-05/new-report-highlights-hospital-mortality-rates/5135858) are concerned about hospital performance with regards to outcome after stroke. Various groups including those from health-care information company, Dr Foster, have considered the Charlson comorbidity index (CCI) as a way to measure hospital performance. This has been done in the hope that better measurements would lead to improvement in care (2, 5, 6). The CCI acted as a weight in the calculation of the standardized hospital mortality rate. Within Australia, there are several groups that used comorbidity index for monitoring hospital performance (7). Some Australian hospitals have been named for “higher than expected mortality rate” for stroke and heart attacks (http://www.abc.net.au/news/2013-12-05/new-report-highlights-hospital-mortality-rates/5135858).
The CCI was conceived as a method for classifying prognostic comorbidity in longitudinal studies (8). It is a weighted index of comorbid conditions and is extracted from data entered into hospital medical records (9). This action is usually performed by administrative rather than clinical staff. Investigators have also developed method to collect CCI from electronic medical records (10). The earlier optimism of different types of comorbidity indices, as risk adjustment for the prediction of mortality after stroke (11, 12), has been recently questioned by several investigators (13–15) including the team from Dr Foster in 2016 (16). This may have occurred because CCI captures comorbid conditions in general but contains only a small component for capturing the effects of stroke.
The value of the covariate CCI in the prediction of disability after ischemic stroke is less well understood and is the subject of this study. Previous investigators have described that patients with low CCI had better outcome at discharge than those with high CCI (17, 18). However, these authors had not adjusted for stroke severity or other variables such as thrombolytic therapy with recombinant tissue plasminogen activator (rTPA). In a small study (n = 133), investigators have suggested that women with higher CCI have greater disability after stroke after adjusting for stroke severity (19). Findings from this study (20) and the earlier study, from the same group, (18) on CCI had been recently incorporated into a proposed a neuroeconomic approach towards decision making on rTPA therapy (21). In light of the multiple uses of CCI, the aims of this study were to determine the minimum clinical data set to be included in model predicting disability after ischemic stroke after adjusting for CCI and clinical variables and to evaluate the impact of CCI on prediction of disability outcome. To achieve this purpose, we have identified clinical trial repository such as the Virtual International Stroke Trials Archive (VISTA) (21) as having data on stroke deficit, comorbidity, and outcome.
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