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Abstract Details

Garbage in, Garbage Out—Accuracy of Stroke Discharge Documentation and Impact on Big Data
Cerebrovascular Disease and Interventional Neurology
Cerebrovascular Disease and Interventional Neurology Posters (7:00 AM-5:00 PM)
171

To assess the accuracy of acute ischemic stroke (AIS) subtype identification.
Accurate identification of the etiology of AIS is important for both patient outcomes and research integrity. Part of certification as a Comprehensive Stroke Center is submission of stroke etiology into the Get With The Guidelines Database. GWTG uses the TOAST classification system, which identifies five subtypes: 1) large-artery atherosclerosis, 2) cardioembolism, 3) small-vessel occlusion, 4) stroke of other determined etiology, and 5) stroke of undetermined etiology. Inaccurate subtyping may lead to excessive diagnostic testing, inappropriate treatment, and inaccuracies in big data.
This is a single center retrospective chart review of adult patients >18 years of age admitted with a diagnosis of AIS between January 1, 2017 and December 31, 2018 (n=204). Patient demographics, brain imaging studies, admission and discharge NIH stroke scales, and documented stroke etiology at discharge were recorded. Etiology after review was determined by the authors based on clinical, historical, and imaging features. The first author was trained to recognize AIS subtypes based on imaging patterns and comorbid medical conditions. Final review was performed by a certified Vascular Neurologist. Congruence between documented stroke etiology at discharge and etiology after review was evaluated.

A total of 171 charts were included for analysis, with 33 excluded due to incomplete information. Of the 171 documented stroke etiologies at discharge, 116 (67.8%) were congruent with etiology after review. Of the 32.2% (n=55) that were incongruent, the most commonly inaccurate designation was large artery disease, followed by undetermined etiology.

Inaccurate identification of AIS subtype is common. The results of this study suggest the need for a standardized systematic process for identifying etiology of AIS and further evaluation of factors affecting inaccuracy, such as level of training of the documenter, to prevent downstream errors in data used for stroke research.
Authors/Disclosures

PRESENTER 		No disclosure on file
Jessica D. Lee, MD, FAAN (University of Kentucky) Dr. Lee has received personal compensation for serving as an employee of 好色先生. The institution of Dr. Lee has received research support from Janssen.