Intravenous thrombolytic therapy (TT) improves outcomes in acute ischemic stroke (AIS), when delivered within 3 hours of symptom-onset Yet, despite the overall benefits, there remains a significant risk of thrombolytic-related intracranial hemorrhage (ICH), even within the 3-hour time frame. And multiple trials including patients beyond this time-window have failed to find benefit for TT in AIS. In part because of the narrow time-window of therapeutic opportunity, less than 5% of all AIS patients are currently treated with TT. Our preliminary work has demonstrated that the absence of overall benefit from TT when administered after 3 hours from symptom-onset obscures the fact that some patients are still likely to benefit, while others are more likely to be harmed. Further, these patient sub-groups can be identified on the basis of pre-treatment clinical characteristics, but only when multiple characteristics are considered simultaneously. Based on our mathematical models that identify patients with AIS who have a favorable risk-benefit profile for TT, and working closely with experienced human factors engineers and the ultimate end-users (ie stroke neurologists and emergency physicians), we propose to develop a set of tools incorporated into a computer-based decision-support instrument (CDS1) for use in real time. The core component of this CDSI is the patient-selection module, to help select patients with a favorable risk-benefit profile for TT. Thus, the specific aims of this project are: 1. To incorporate our developed mathematical models for patient-selection into a usable CDSI. 2. Through an iterative design cycle, including several phases of usability testing, to develop this prototype into a comprehensive decision-support instrument with a well-designed a user interface, well-integrated into usual work-flow, and supporting a range of functions important for acute stroke care. 3. To plan a pilot study to evaluate the feasibility of """"""""real-time assisted multi-dimensional patient selection"""""""" to select AIS patients who may benefit from TT when treated more than 3 hours from symptom-onset. While the results of this study should have important implications for the use of TT in AIS, we anticipate that this new method of """"""""assisted multi-dimensional patient-selection"""""""" may have profound implications in many areas, especially for treatments in which the risks and the benefits are finely balanced.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21NS048225-02
Application #
6872019
Study Section
Clinical Neuroscience and Disease Study Section (CND)
Program Officer
Jacobs, Tom P
Project Start
2004-04-01
Project End
2006-09-30
Budget Start
2005-04-01
Budget End
2006-09-30
Support Year
2
Fiscal Year
2005
Total Cost
$184,058
Indirect Cost
Name
Tufts University
Department
Type
DUNS #
079532263
City
Boston
State
MA
Country
United States
Zip Code
02111