Risk Score for In-Hospital Ischemic Stroke Mortality Derived and Validated Within the Get With The Guidelines–Stroke Program

Author:

Smith Eric E.1,Shobha Nandavar1,Dai David1,Olson DaiWai M.1,Reeves Mathew J.1,Saver Jeffrey L.1,Hernandez Adrian F.1,Peterson Eric D.1,Fonarow Gregg C.1,Schwamm Lee H.1

Affiliation:

1. From the Calgary Stroke Program (E.E.S., N.S.), Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Duke Clinical Research Institute (D.D., D.M.O., A.F.H., E.D.P.), Durham, NC; Department of Epidemiology (M.J.R.), Michigan State University, East Lansing; Department of Neurology (J.L.S.) and Division of Cardiology (G.C.F.), University of California, Los Angeles; and Stroke Service (L.H.S.), Massachusetts General Hospital, Boston, Mass.

Abstract

Background— 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. Methods and Results— 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. Conclusions— 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.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Physiology (medical),Cardiology and Cardiovascular Medicine

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3