Publications by authors named "Arash Niktabe"
Hyperglycemia Is Associated With Computed Tomography Perfusion Core Volume Underestimation in Patients With Acute Ischemic Stroke With Large-Vessel Occlusion.
Stroke Vasc Interv Neurol
July 2024
Background: CT Perfusion (CTP) predictions of infarct core play an important role in the determination of treatment eligibility in large vessel occlusion (LVO) acute ischemic stroke (AIS). Prior studies have demonstrated that blood glucose can affect cerebral blood flow (CBF). Here we examine the influence of acute and chronic hyperglycemia on CTP estimations of infarct core.
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- The study examines how adding endovascular stroke therapy (EST) capabilities in community hospitals affects acute ischemic stroke (AIS) admissions in a large hospital system over a period from 2014 to 2022.! -
- Data gathered from 10 hospitals showed that community hospitals transitioning to EST-performing hospitals (EPHs) experienced a 1.9% monthly increase in AIS admissions, particularly for non-large vessel occlusion strokes, while an established EPH saw a decline in admissions and treatment rates.! -
- The findings suggest that enhancing EST availability in community settings can lead to increased AIS admissions, highlighting a shift in patient care dynamics when hospitals gain EST capabilities.!
Article Synopsis
- The study investigates how using automated interpretations of CT scans and secure messaging can speed up the process of endovascular stroke therapy (EVT) for large vessel occlusion (LVO) strokes in hospitals.
- Conducted at four comprehensive stroke centers in Houston, Texas, the trial involved 443 patients with LVO strokes and aimed to reduce critical time metrics such as door-to-groin (DTG) time after implementing AI technology.
- The primary outcome measured was the impact on DTG time using statistical models, while secondary outcomes included the time from hospital arrival to treatment and patient recovery metrics at 90 days.
Acute ischemic stroke is a leading cause of death and disability in the world. Treatment decisions, especially around emergent revascularization procedures, rely heavily on size and location of the infarct core. Currently, accurate assessment of this measure is challenging.
View Article and Find Full Text PDFBackground: In recent years, machine learning (ML) has had notable success in providing automated analyses of neuroimaging studies, and its role is likely to increase in the future. Thus, it is paramount for clinicians to understand these approaches, gain facility with interpreting ML results, and learn how to assess algorithm performance.
Objective: To provide an overview of ML, present its role in acute stroke imaging, discuss methods to evaluate algorithms, and then provide an assessment of existing approaches.