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J Stroke Cerebrovasc Dis. 2016 Apr;25(4):792-8. doi: 10.1016/j.jstrokecerebrovasdis.2015.12.002. Epub 2016 Jan 13.

Using Radiological Data to Estimate Ischemic Stroke Severity.

Author information

1
Neurology Service, VA Connecticut Healthcare System, West Haven, Connecticut; Department of Neurology and Center for NeuroEpidemiological and Clinical Neurological Research, Yale University School of Medicine, New Haven, Connecticut; Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut. Electronic address: Jason.sico@yale.edu.
2
Department of Neurology, University of Maryland, Baltimore, Maryland; Department of Epidemiology and Public Health, University of Maryland, Baltimore, Maryland.
3
Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut; Medicine Service, VA Connecticut Healthcare System, West Haven, Connecticut; Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, Connecticut.
4
Department of Anesthesiology, Yale University School of Medicine, New Haven, Connecticut.
5
Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut; Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, Connecticut.
6
Department of Neurology, Warren Alpert School of Medicine and Departments of Neurosciences, Community Health, and Engineering at Brown University, Providence, Rhode Island; Providence Veterans Administration Medical Center, Providence, Rhode Island.
7
Neurology Service, Malcolm Randall VA Medical Center, Gainesville, Florida; Department of Neurology, University of Florida School of Medicine, Gainesville, Florida.
8
VA Health Services Research and Development (HSR&D) Center for Health Information and Communication (CHIC) and the HSR&D Stroke Quality Enhancement Research Initiative (QUERI), Richard L. Roudebush VA Medical Center, Indianapolis, Indiana; Department of Neurology, Indiana University School of Medicine, Regenstrief Institute, Indianapolis, Indiana.
9
Department of Neurology, University of Vermont School of Medicine, Burlington, Vermont.
10
Medical Service, Boise VA Medical Center, Boise, Virginia.
11
VA Health Services Research and Development (HSR&D) Center for Health Information and Communication (CHIC) and the HSR&D Stroke Quality Enhancement Research Initiative (QUERI), Richard L. Roudebush VA Medical Center, Indianapolis, Indiana; Department of Internal Medicine, Indiana University School of Medicine, Regenstrief Institute, Indianapolis, Indiana.

Abstract

BACKGROUND:

Risk-adjusted poststroke mortality has been proposed for use as a measure of stroke care quality. Although valid measures of stroke severity (e.g., the National Institutes of Health Stroke Scale [NIHSS]) are not typically available in administrative datasets, radiology reports are often available within electronic health records. We sought to examine whether admission head computed tomography data could be used to estimate stroke severity.

MATERIALS AND METHODS:

Using chart review data from a cohort of acute ischemic stroke patients (1998-2003), we developed a radiographic measure ([BIS]) of stroke severity in a two-third development set and assessed in a one-third validation set. The retrospective NIHSS was dichotomized as mild/moderate (<10) and severe (≥10). We compared the association of this radiographic score with NIHSS and in-hospital mortality at the patient level.

RESULTS:

Among 1348 stroke patients, 86.5% had abnormal findings on initial head computed tomography. The c-statistic for the BIS for modeling severe stroke (development, .581; validation, .579) and in-hospital mortality (development, .623; validation, .678) were generated.

CONCLUSIONS:

Although the c-statistics were only moderate, the BIS provided significant risk stratification information with a 2-variable score. Until administrative data routinely includes a valid measure of stroke severity, radiographic data may provide information for use in risk adjustment.

KEYWORDS:

CT scan; Ischemic stroke; NIHSS; imaging

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