Using clinical and thrombus characteristics to predict the etiology of ischemic stroke

Authors

• Graham A. Branscom, Icahn School of Medicine at Mount SinaiFollow
• Carina Seah, Icahn School of Medicine at Mount SinaiFollow
• Tannishtha Som, Icahn School of Medicine at Mount SinaiFollow
• James Vicari, Icahn School of Medicine at Mount SinaiFollow
• Alan R. Dabney, Texas AandM UniversityFollow
• Selva Baltan, Oregon Health and Science UniversityFollow
• Farida Sohrabji, Texas AandM UniversityFollow
• Keith Pennypacker, University of KentuckyFollow
• Keith B. Woodward, Fort Sanders Regional Medical CenterFollow
• Benjamin I. Rapoport, Icahn School of Medicine at Mount SinaiFollow
• Sol Schulman, Harvard UniversityFollow
• Justin F. Fraser, University of KentuckyFollow
• Christopher P. Kellner, Icahn School of Medicine at Mount SinaiFollow

Abstract

Background and purpose: Ischemic stroke comprises about 87% of all stroke cases in the US. 20% of these have a cardioembolic (CE) etiology, and 25% are still considered embolic strokes of undetermined sources (ESUS). ESUS strokes are associated with worse functional outcomes and survival rates, underscoring the need for understanding the clinical differences in stroke etiology. Methods: We analyzed the Investigation of Clot in Ischemic Stroke and Hematoma Evacuation (INSIGHT) registry (NCT04693767, https://clinicaltrials.gov/study/NCT04693767), which includes n=388 patients total (n=208 CE, n=142 AFIB, n=180 non-CE). We analyzed 123 data features, spanning across domains including demographics, past medical history, baseline blood lab values, intraoperative data, and thrombus characteristics. We found correlations, univariate analyses, and LASSO machine learning models to predict etiology. Results: We found several associations between clinical variables and stroke etiology. Higher baseline prothrombin time and INR (only among patients not on anticoagulants), baseline use of antiarrhythmics and antihypertensives, and a higher baseline mRS were associated with an increased risk of both overall CE and AFIB-specific etiologies. A higher baseline platelet and RBC count was associated with a decreased risk of AFIB stroke etiology. All of these results were significant (p<0.05) across multiple analysis methods. Lastly, our LASSO models predicted CE and AFIB-specific etiologies with an AUC of 0.71 and 0.73, respectively. Conclusions: Several clinical features are strongly correlated with stroke etiology, and these can be combined in a machine learning model to predict stroke etiology.

Document Type

Article

Publication Date

7-1-2026

Notes/Citation Information

Publisher Copyright: © 2026 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license. http://creativecommons.org/licenses/by-nc-nd/4.0/

Digital Object Identifier (DOI)

10.1016/j.jstrokecerebrovasdis.2026.108665

Archival?

Archival

Repository Citation

Branscom, Graham A.; Seah, Carina; Som, Tannishtha; Vicari, James; Dabney, Alan R.; Baltan, Selva; Sohrabji, Farida; Pennypacker, Keith; Woodward, Keith B.; Rapoport, Benjamin I.; Schulman, Sol; Fraser, Justin F.; and Kellner, Christopher P., "Using clinical and thrombus characteristics to predict the etiology of ischemic stroke" (2026). Neurology Faculty Publications. 142.
https://uknowledge.uky.edu/neurology_facpub/142