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      Relationship of National Institutes of Health Stroke Scale to 30-Day Mortality in Medicare Beneficiaries With Acute Ischemic Stroke

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          Abstract

          Background

          The National Institutes of Health Stroke Scale (NIHSS), a well-validated tool for assessing initial stroke severity, has previously been shown to be associated with mortality in acute ischemic stroke. However, the relationship, optimal categorization, and risk discrimination with the NIHSS for predicting 30-day mortality among Medicare beneficiaries with acute ischemic stroke has not been well studied.

          Methods and Results

          We analyzed data from 33102 fee-for-service Medicare beneficiaries treated at 404 Get With The Guidelines-Stroke hospitals between April 2003 and December 2006 with NIHSS documented. The 30-day mortality rate by NIHSS as a continuous variable and by risk-tree determined or prespecified categories were analyzed, with discrimination of risk quantified by the c-statistic. In this cohort, mean age was 79.0 years and 58% were female. The median NIHSS score was 5 (25th to 75th percentile 2 to 12). There were 4496 deaths in the first 30 days (13.6%). There was a strong graded relation between increasing NIHSS score and higher 30-day mortality. The 30-day mortality rates for acute ischemic stroke by NIHSS categories were as follows: 0 to 7, 4.2%; 8 to 13, 13.9%; 14 to 21, 31.6%; 22 to 42, 53.5%. A model with NIHSS alone provided excellent discrimination whether included as a continuous variable ( c-statistic 0.82 [0.81 to 0.83]), 4 categories ( c-statistic 0.80 [0.79 to 0.80]), or 3 categories ( c-statistic 0.79 [0.78 to 0.79]).

          Conclusions

          The NIHSS provides substantial prognostic information regarding 30-day mortality risk in Medicare beneficiaries with acute ischemic stroke. This index of stroke severity is a very strong discriminator of mortality risk, even in the absence of other clinical information, whether used as a continuous or categorical risk determinant. ( J Am Heart Assoc. 2012;1:42-50.)

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          Most cited references20

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          Use and misuse of the receiver operating characteristic curve in risk prediction.

          The c statistic, or area under the receiver operating characteristic (ROC) curve, achieved popularity in diagnostic testing, in which the test characteristics of sensitivity and specificity are relevant to discriminating diseased versus nondiseased patients. The c statistic, however, may not be optimal in assessing models that predict future risk or stratify individuals into risk categories. In this setting, calibration is as important to the accurate assessment of risk. For example, a biomarker with an odds ratio of 3 may have little effect on the c statistic, yet an increased level could shift estimated 10-year cardiovascular risk for an individual patient from 8% to 24%, which would lead to different treatment recommendations under current Adult Treatment Panel III guidelines. Accepted risk factors such as lipids, hypertension, and smoking have only marginal impact on the c statistic individually yet lead to more accurate reclassification of large proportions of patients into higher-risk or lower-risk categories. Perfectly calibrated models for complex disease can, in fact, only achieve values for the c statistic well below the theoretical maximum of 1. Use of the c statistic for model selection could thus naively eliminate established risk factors from cardiovascular risk prediction scores. As novel risk factors are discovered, sole reliance on the c statistic to evaluate their utility as risk predictors thus seems ill-advised.
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            Age and National Institutes of Health Stroke Scale Score within 6 hours after onset are accurate predictors of outcome after cerebral ischemia: development and external validation of prognostic models.

            To date, no validated, comprehensive, and practicable model exists to predict functional recovery within the first hours of cerebral ischemic symptoms. The purpose of this study was to externally validate 2 prognostic models predicting functional outcome and survival at 100 days within the first 6 hours after onset of acute cerebral ischemia. On admission to a participating hospital, patients were registered prospectively and included according to defined criteria. Follow-up was performed 100 days after the event. With the use of prospectively collected data, 2 prognostic models were developed and internally calibrated in 1079 patients and externally validated in 1307 patients. By means of age and National Institutes of Health Stroke Scale (NIHSS) score as independent variables, model I predicts incomplete functional recovery (Barthel Index <95) versus complete functional recovery, and model II predicts mortality versus survival. In the validation data set, model I correctly predicted 62.9% of the patients who were incompletely restituted or had died and 83.2% of the completely restituted patients, and model II correctly predicted 57.9% of the patients who had died and 91.5% of the surviving patients. Both models performed better than the treating physicians' predictions made within 6 hours after admission. The resulting prognostic models are useful to correctly stratify treatment groups in clinical trials and should guide inclusion criteria in clinical trials, which in turn increases the power to detect clinically relevant differences.
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              Risk score for in-hospital ischemic stroke mortality derived and validated within the Get With the Guidelines-Stroke Program.

              There are few validated models for prediction of in-hospital mortality after ischemic stroke. We used Get With the Guidelines-Stroke Program data to derive and validate prediction models for a patient's risk of in-hospital ischemic stroke mortality. Between October 2001 and December 2007, there were 1036 hospitals that contributed 274,988 ischemic stroke patients to this study. The sample was randomly divided into a derivation (60%) and validation (40%) sample. Logistic regression was used to determine the independent predictors of mortality and to assign point scores for a prediction model. We also separately derived and validated a model in the 109,187 patients (39.7%) with a National Institutes of Health Stroke Scale (NIHSS) score recorded. Model discrimination was quantified by calculating the C statistic from the validation sample. In-hospital mortality was 5.5% overall and 5.2% in the subset in which NIHSS score was recorded. Characteristics associated with in-hospital mortality were age, arrival mode (eg, via ambulance versus other mode), history of atrial fibrillation, previous stroke, previous myocardial infarction, carotid stenosis, diabetes mellitus, peripheral vascular disease, hypertension, history of dyslipidemia, current smoking, and weekend or night admission. The C statistic was 0.72 in the overall validation sample and 0.85 in the model that included NIHSS score. A model with NIHSS score alone provided nearly as good discrimination (C statistic 0.83). Plots of observed versus predicted mortality showed excellent model calibration in the validation sample. The Get With the Guidelines-Stroke risk model provides clinicians with a well-validated, practical bedside tool for mortality risk stratification. The NIHSS score provides substantial incremental information on a patient's short-term mortality risk and is the strongest predictor of mortality.
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                Author and article information

                Journal
                J Am Heart Assoc
                J Am Heart Assoc
                ahaoa
                JAH3
                Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
                Blackwell Publishing Ltd
                2047-9980
                February 2012
                20 February 2012
                : 1
                : 1
                : 42-50
                Affiliations
                Division of Cardiology, University of California, Los Angeles, CA (G.C.F.)
                Department of Neurology, University of California, Los Angeles (J.L.S.)
                Department of Clinical Neurosciences, University of Calgary, Alberta, Canada (E.E.S.)
                Department of Neurology, University of Cincinnati Academic Health Center, OH (J.P.B., D.O.K.)
                Miller School of Medicine, University of Miami, FL (R.L.S.)
                Duke Clinical Research Center, Durham, NC (W.P., D.M.O., A.F.H., E.D.P.)
                Division of Neurology, Massachusetts General Hospital, Boston (L.H.S.)
                Author notes
                Correspondence to: Gregg C. Fonarow, MD, Ahmanson-UCLA Cardiomyopathy Center, Ronald Reagan UCLA Medical Center, 10833 LeConte Avenue, Room 47-123 CHS, Los Angeles, CA 90095. E-mail: gfonarow@ 123456mednet.ucla.edu
                Article
                jah31
                10.1161/JAHA.111.000034
                3487316
                23130117
                6a924afb-8a18-46bc-aa6f-a6d91506d5ee
                © 2012 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley-Blackwell.

                This is an Open Access article under the terms of the Creative Commons Attribution Noncommercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                Categories
                Original Research
                Stroke

                Cardiovascular Medicine
                national institutes of health stroke scale,mortality,ischemic stroke,registries

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