Table of Contents
ISRN Stroke
Volume 2013, Article ID 681673, 8 pages
Research Article

Hemorrhagic Transformation (HT) and Symptomatic Intracerebral Hemorrhage (sICH) Risk Prediction Models for Postthrombolytic Hemorrhage in the Stroke Belt

1Stroke Program, Department of Neurology, Tulane University School of Medicine, New Orleans, LA 70112, USA
2Stroke Program, Department of Neurology, School of Medicine, University of Alabama at Birmingham, RWUH 226M, 1720 2nd Avenue S, Birmingham, AL 35249, USA
3Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Al 35249, USA
4Health Services and Outcomes Research Center for Outcome and Effectiveness Research and Education (COERE), Birmingham, AL 35249, USA
5Center of Excellence in Comparative Effectiveness Research for Eliminating Disparities (CERED), Minority Health & Health Disparities Research Center (MHRC), Birmingham, AL 35249, USA
6School of Nursing, University of Alabama at Birmingham, AL 35249, USA

Received 30 August 2013; Accepted 27 September 2013

Academic Editors: F. Corea, S. Lorenzano, M. O. McCarron, M. Paciaroni, and A. Slivka

Copyright © 2013 James E. Siegler et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background. Symptomatic intracerebral hemorrhage (sICH) remains the most feared complication of intravenous tissue plasminogen activator (IV tPA) treatment. We aimed to investigate how previously validated scoring methodologies would perform in treated patients in two US Stroke Belt states. Methods and Results. We retrospectively reviewed consecutive patients from two centers in two Stroke Belt states who received IV tPA (2008–2011). We assessed the ability of three models to predict sICH. sICH was defined as a type 2 parenchymal hemorrhage with deterioration in National Institutes of Health Stroke Scale (NIHSS) score of ≥4 points or death. Among 457 IV tPA-treated patients, 19 (4.2%) had sICH (mean age 68, 26.3% Black, 63.2% female). The Cucchiara model was most predictive of sICH in the entire cohort (AUC: 0.6528) and most predictive of sICH among Blacks (OR = 6.03, 95% CI 1.07–34.1, ) when patients were dichotomized by score. Conclusions. In our small sample from the racially heterogeneous US Stroke Belt, the Cucchiara model outperformed the other models at predicting sICH. While predictive models should not be used to justify nontreatment with thrombolytics, those interested in understanding contributors to sICH may choose to use the Cucchiara model until a Stroke Belt model is developed for this region.