Kevin N. Sheth

May 16, 2024 | nature.com | Ho-Joon Lee |Lee H. Schwamm |Kevin N. Sheth |Smita Krishnaswamy |Hongyu Zhao |Harlan M. Krumholz

AbstractDetermining acute ischemic stroke (AIS) etiology is fundamental to secondary stroke prevention efforts but can be diagnostically challenging. We trained and validated an automated classification tool, StrokeClassifier, using electronic health record (EHR) text from 2039 non-cryptogenic AIS patients at 2 academic hospitals to predict the 4-level outcome of stroke etiology adjudicated by agreement of at least 2 board-certified vascular neurologists’ review of the EHR.

Feb 9, 2024 | jamanetwork.com | Kevin N. Sheth |Nicole Solomon |Brooke Alhanti |Steven R. Messé

Key PointsQuestion  Is door-to-treatment time associated with improved outcomes in patients with anticoagulation-associated intracerebral hemorrhage receiving reversal interventions? Findings  In this cohort study including 9492 patients, a door-to-treatment time of 60 minutes or less was associated with lower mortality and hospice transfers. White race and stroke severity were associated with faster administration of reversal interventions.

Feb 5, 2024 | nature.com | Anh Tran |Tal Zeevi |Gaby Abou Karam |Kevin N. Sheth

AbstractHematoma expansion (HE) is a modifiable risk factor and a potential treatment target in patients with intracerebral hemorrhage (ICH). We aimed to train and validate deep-learning models for high-confidence prediction of supratentorial ICH expansion, based on admission non-contrast head Computed Tomography (CT). Applying Monte Carlo dropout and entropy of deep-learning model predictions, we estimated the model uncertainty and identified patients at high risk of HE with high confidence.