Predicting disability after ischemic stroke based on comorbidity index and stroke severity-from the virtual international stroke trials archive-acute collaboration

Thanh G. Phan, Benjamin B. Clissold, Henry Ma, John Van Ly, Velandai Srikanth, K. R. Lees, A. Alexandrov, P. M. Bath, E. Bluhmki, N. Bornstein, C. Chen, L. Claesson, S. M. Davis, G. Donnan, H. C. Diener, M. Fisher, M. Ginsberg, B. Gregson, J. Grotta, W. Hacke & 14 others M. G. Hennerici, M. Hommel, M. Kaste, P. Lyden, J. Marler, K. Muir, N. Venketasubramanian, R. Sacco, A. Shuaib, P. Teal, N. G. Wahlgren, S. Warach, C. Weimar, on Behalf of the VISTA-Acute Collaborators

Research output: Contribution to journalArticle

3 Citations (Scopus)

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 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.

Original languageEnglish (US)
Article number192
JournalFrontiers in Neurology
Volume8
Issue numberMAY
DOIs
StatePublished - May 19 2017

Fingerprint

Comorbidity
Stroke
National Institutes of Health (U.S.)
Area Under Curve
Tissue Plasminogen Activator
ROC Curve
Calibration
Logistic Models
Clinical Trials
Mortality

Keywords

  • Charlson comorbidity score
  • Disability evaluation
  • National Institutes of Health Stroke Scale scores
  • Prediction
  • Stroke

ASJC Scopus subject areas

  • Neurology
  • Clinical Neurology

Cite this

Phan, T. G., Clissold, B. B., Ma, H., Van Ly, J., Srikanth, V., Lees, K. R., ... on Behalf of the VISTA-Acute Collaborators (2017). Predicting disability after ischemic stroke based on comorbidity index and stroke severity-from the virtual international stroke trials archive-acute collaboration. Frontiers in Neurology, 8(MAY), [192]. https://doi.org/10.3389/fneur.2017.00192

Predicting disability after ischemic stroke based on comorbidity index and stroke severity-from the virtual international stroke trials archive-acute collaboration. / Phan, Thanh G.; Clissold, Benjamin B.; Ma, Henry; Van Ly, John; Srikanth, Velandai; Lees, K. R.; Alexandrov, A.; Bath, P. M.; Bluhmki, E.; Bornstein, N.; Chen, C.; Claesson, L.; Davis, S. M.; Donnan, G.; Diener, H. C.; Fisher, M.; Ginsberg, M.; Gregson, B.; Grotta, J.; Hacke, W.; Hennerici, M. G.; Hommel, M.; Kaste, M.; Lyden, P.; Marler, J.; Muir, K.; Venketasubramanian, N.; Sacco, R.; Shuaib, A.; Teal, P.; Wahlgren, N. G.; Warach, S.; Weimar, C.; on Behalf of the VISTA-Acute Collaborators.

In: Frontiers in Neurology, Vol. 8, No. MAY, 192, 19.05.2017.

Research output: Contribution to journalArticle

Phan, TG, Clissold, BB, Ma, H, Van Ly, J, Srikanth, V, Lees, KR, Alexandrov, A, Bath, PM, Bluhmki, E, Bornstein, N, Chen, C, Claesson, L, Davis, SM, Donnan, G, Diener, HC, Fisher, M, Ginsberg, M, Gregson, B, Grotta, J, Hacke, W, Hennerici, MG, Hommel, M, Kaste, M, Lyden, P, Marler, J, Muir, K, Venketasubramanian, N, Sacco, R, Shuaib, A, Teal, P, Wahlgren, NG, Warach, S, Weimar, C & on Behalf of the VISTA-Acute Collaborators 2017, 'Predicting disability after ischemic stroke based on comorbidity index and stroke severity-from the virtual international stroke trials archive-acute collaboration', Frontiers in Neurology, vol. 8, no. MAY, 192. https://doi.org/10.3389/fneur.2017.00192
Phan, Thanh G. ; Clissold, Benjamin B. ; Ma, Henry ; Van Ly, John ; Srikanth, Velandai ; Lees, K. R. ; Alexandrov, A. ; Bath, P. M. ; Bluhmki, E. ; Bornstein, N. ; Chen, C. ; Claesson, L. ; Davis, S. M. ; Donnan, G. ; Diener, H. C. ; Fisher, M. ; Ginsberg, M. ; Gregson, B. ; Grotta, J. ; Hacke, W. ; Hennerici, M. G. ; Hommel, M. ; Kaste, M. ; Lyden, P. ; Marler, J. ; Muir, K. ; Venketasubramanian, N. ; Sacco, R. ; Shuaib, A. ; Teal, P. ; Wahlgren, N. G. ; Warach, S. ; Weimar, C. ; on Behalf of the VISTA-Acute Collaborators. / Predicting disability after ischemic stroke based on comorbidity index and stroke severity-from the virtual international stroke trials archive-acute collaboration. In: Frontiers in Neurology. 2017 ; Vol. 8, No. MAY.
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title = "Predicting disability after ischemic stroke based on comorbidity index and stroke severity-from the virtual international stroke trials archive-acute collaboration",
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 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.",
keywords = "Charlson comorbidity score, Disability evaluation, National Institutes of Health Stroke Scale scores, Prediction, Stroke",
author = "Phan, {Thanh G.} and Clissold, {Benjamin B.} and Henry Ma and {Van Ly}, John and Velandai Srikanth and Lees, {K. R.} and A. Alexandrov and Bath, {P. M.} and E. Bluhmki and N. Bornstein and C. Chen and L. Claesson and Davis, {S. M.} and G. Donnan and Diener, {H. C.} and M. Fisher and M. Ginsberg and B. Gregson and J. Grotta and W. Hacke and Hennerici, {M. G.} and M. Hommel and M. Kaste and P. Lyden and J. Marler and K. Muir and N. Venketasubramanian and R. Sacco and A. Shuaib and P. Teal and Wahlgren, {N. G.} and S. Warach and C. Weimar and {on Behalf of the VISTA-Acute Collaborators}",
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TY - JOUR

T1 - Predicting disability after ischemic stroke based on comorbidity index and stroke severity-from the virtual international stroke trials archive-acute collaboration

AU - Phan, Thanh G.

AU - Clissold, Benjamin B.

AU - Ma, Henry

AU - Van Ly, John

AU - Srikanth, Velandai

AU - Lees, K. R.

AU - Alexandrov, A.

AU - Bath, P. M.

AU - Bluhmki, E.

AU - Bornstein, N.

AU - Chen, C.

AU - Claesson, L.

AU - Davis, S. M.

AU - Donnan, G.

AU - Diener, H. C.

AU - Fisher, M.

AU - Ginsberg, M.

AU - Gregson, B.

AU - Grotta, J.

AU - Hacke, W.

AU - Hennerici, M. G.

AU - Hommel, M.

AU - Kaste, M.

AU - Lyden, P.

AU - Marler, J.

AU - Muir, K.

AU - Venketasubramanian, N.

AU - Sacco, R.

AU - Shuaib, A.

AU - Teal, P.

AU - Wahlgren, N. G.

AU - Warach, S.

AU - Weimar, C.

AU - on Behalf of the VISTA-Acute Collaborators

PY - 2017/5/19

Y1 - 2017/5/19

N2 - 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.

AB - 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.

KW - Charlson comorbidity score

KW - Disability evaluation

KW - National Institutes of Health Stroke Scale scores

KW - Prediction

KW - Stroke

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