Predicting 24-Hour Blood Pressure Variability Post Thrombectomy Using Machine Learning for Patients with Ischemic Stroke from Anterior Circulation Large Vessel Occlusion
This study identified the interval from last-known-well time-to-groin puncture, age, and NIHSS as factors significantly associated with increased 24-hour BPV in random forest analysis. These predisposing factors in our machine learning analysis allow clinicians to identify patients who are at risk of having increased BPV and opportunities to augment these patients' blood pressure control.
Authors
- Melissa Pergakis (ORCID: https://orcid.org/0000-0003-4526-4176)
- Quincy Tran (ORCID: https://orcid.org/0000-0002-7774-154X)
- Chad Schrier
- Daniel Najafali (ORCID: https://orcid.org/0000-0003-1509-9229)
- Thomas Johnstone (ORCID: https://orcid.org/0000-0003-2666-0608)
- Taylor Kowansky (ORCID: https://orcid.org/0009-0003-1898-7261)
- Adelina Buganu
- Sanjeev Herr (ORCID: https://orcid.org/0000-0002-4302-2854)
- Gaurav Jindal (ORCID: https://orcid.org/0000-0002-2124-902X)
- Megan Najafali (ORCID: https://orcid.org/0000-0001-7997-0664)
- Shriya Jaddu
- Nabih M. Ramadan
Institutions
- University of Maryland, Baltimore (US)
- Christiana Care Health System (US)
- St. Luke's University Health Network (US)
- Carle Foundation Hospital (US)
- University of Maryland Medical Center (US)
- University of Illinois Urbana-Champaign (US)
- Loyola University Chicago (US)
- University of Illinois System (US)
- Drexel University (US)
- Stanford University (US)
Publication Details
- Journal
- World Neurosurgery
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1016/j.wneu.2025.123787
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00