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(Stroke. 2009;40:1332.)
© 2009 American Heart Association, Inc.
Original Contributions |

From the Departments of Neurology (K.C.J., K.M.B.) and Public Health Sciences (K.C.J., D.P.W.), University of Virginia, Charlottesville; and the Department of Radiology (Y.H.D.), Mayo Clinic, Rochester, NY.
Correspondence to Karen C. Johnston, MD, MSc, University of Virginia Health System, Department of Neurology, #800394, Charlottesville, VA 22908-0394. E-mail kj4v{at}virginia.edu
| Abstract |
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Methods— The prospective Acute Stroke Accurate Prediction (ASAP) trial included a prespecified subgroup evaluated for early outcome. Logistic regression analysis was used to assess the prediction of modified Rankin (mRankin) of 0 or 1.
Results— A total of 204 subjects completed the substudy, and 116 (57%) had excellent outcome at 3 months. The area under the ROC curve (AUC) for day-5 NIHSS predicting 3-month excellent outcome was 0.84; for DWI volume predicting outcome was 0.76, and for the multivariable model combining both was 0.84.
Conclusions— The results of the early outcome substudy of the ASAP trial suggest that early stroke severity and infarct volume measures are predictive of 3-month excellent outcome. In our data set the DWI volume does not add clinically relevant information in predicting 3-month outcome. Validation of these results is required.
Key Words: cerebral ischemia prognosis stroke outcome models statistical surrogate
| Introduction |
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| Subject and Methods |
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We used univariate regression analysis to assess the association of day-5 variables with 3-month mRankin score. We used multivariable logistic regression to estimate the multivariable relationships. Model performance was measured by AUC for discrimination with success defined as AUC
0.8.7 The multivariable models were adjusted for age and tPA treatment to control for confounding.
| Results |
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An additional analysis adjusting for the potential effect of treatment with tPA did not change the model estimates (data not shown) or the relative statistical importance of NIHSS score or DWI volume.
Age was a statistically significant covariate at the 0.01 level in all models. The AUC increased to 0.82 for the DWI model and to 0.87 for the NIHSS score model and combined model when age was included.
An accompanying online nomogram provides a means to determine the probability of an excellent 90-day clinical outcome for an individual patient using the age-adjusted day-5 NIHSS score (see supplemental Figure I, available online at http://stroke.ahajournals.org).
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| Discussion |
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Based on our data, a scoring system using age-adjusted day-5 NIHSS score to predict functional outcomes is most likely to have the greatest clinical utility. Age-adjustment of the day-5 NIHSS score maximizes predictive accuracy because of the strong independent association of age and mRS. A simple nomogram can provide the adjustment for younger patients with mild to moderate strokes, but should be used cautiously before external validation (supplemental Figure I).
Our study is limited by small sample size, single site of enrollment, and young population with mild strokes. A larger sample may have demonstrated a significant contribution by DWI. The additional predictive power added by imaging was much smaller than estimated and may have resulted from the use of a single volume measure that did not capture information on infarct location or evolution.6 Incorporation of perfusion MR sequences,9 or clinical covariates such as diabetes or prestroke disability may have improved predictive power.
The relationships identified in this study have not been externally validated. As the sample size is small and our cohort was young with mild to moderately severe strokes, these data are only hypothesis generating requiring validation in a more robust data set.
Early clinical status is a strong predictor of 3-month outcome and may be useful in clinical and research settings. For proof of concept studies, use of a day-5 outcome may substantially reduce the time, cost, and frequency of subjects lost to follow-up while allowing an accurate determination of the appropriateness of proceeding to phase III trials. Additionally, this information may provide an imputation method for trials with early outcome information and a small number of patients missing final outcome data. The strong prediction supports a potential role for day-5 outcome. Once validated, our simple nomogram (supplemental Figure I) may be valuable in similar populations and may be useful in trials with adaptive designs and rapid accrual, as they may facilitate early adjustment of pretrial estimates of event rates. These potential benefits may, in some trials, outweigh the disadvantages of an imperfect but highly predictive estimate of 3-month outcome.
| Acknowledgments |
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Sources of Funding
This research was funded by the National Institutes of Health- National Institute of Neurological Disorders and Stroke (NIH-NINDS) (K23NS02168); The ASAP study was funded by the NIH-NINDS (K23NS02168), and Drs Johnston and Wagner received support from this grant.
Disclosures
None.
| Footnotes |
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Deceased. Received June 17, 2008; revision received September 2, 2008; accepted September 26, 2008.
| References |
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2. Johnston Karen C, Wagner Douglas P, Haley EC Jr, Connors Alfred F Combined clinical and imaging information as an early stroke outcome measure. Stroke. 2002; 33: 466–472.
3. Higashida Randall T, Furlan AJ. Trial Design and Reporting Standards for Intra-arterial cerebral thrombolysis for acute ischemic stroke. Stroke. 2003; 34: e109–e137.[CrossRef][Medline] [Order article via Infotrieve]
4. Brown Devin L, Johnston, Karen C. Wagner Douglas P, Haley EC Jr. Predicting major neurological improvement with intravenous recombinant tissue plasminogen activator treatment of stroke. Stroke. 2004; 35: 147–150.
5. Johnston KC, Wagner DP, Wang Xin-Qun, Newman George C, Hijs Vincent, Sen Souvik, Warach Steven for the GAIN, Citicoline and ASAP Investigators. Validation of an acute ischemic stroke model. Does diffusion-weighted imaging lesion volume offer a clinically significant improvement in prediction of outcome? Stroke. 2007; 38: 1820–1825.
6. Baird AE, Benfield A, Schlaug G, Siewert B. Lovblad KO. Edelman RR. Warach S. Enlargement of human cerebral ischemic lesion volumes measured by diffusion-weighted magnetic resonance imaging. Ann Neurol. 1997; 41: 581–589.[CrossRef][Medline] [Order article via Infotrieve]
7. Harrell FE. Regression Modeling Strategies. New York: Springer 2001.
8. Saver JL, Johnston KC, Homer D, Wityk R, Koroshetz W, Truskowski LL, Haley EC and the RANTTAS Investigators. Infarct volume as a surrogate or auxiliary outcome measure in ischemic stroke clinical trials. Stroke. 1999; 30: 293–298.
9. Thijs VN, Adami A, Neumann-Haefelin T, Moseley ME, Marks MP, Albers GW. Relationship between severity of MR perfusion deficit and DWI lesion evolution. Neurology. 2001; 57: 1205–1211.
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