Document Type
Article
Abstract
As many as 80% of critically ill patients develop delirium increasing the need for institutionalization and higher morbidity and mortality. Clinicians detect less than 40% of delirium when using a validated screening tool. EEG is the criterion standard but is resource intensive thus not feasible for widespread delirium monitoring. This study evaluated the use of limited-lead rapid-response EEG and supervised deep learning methods with vision transformer to predict delirium. This proof-of-concept study used a prospective design to evaluate use of supervised deep learning with vision transformer and a rapid-response EEG device for predicting delirium in mechanically ventilated critically ill older adults. Fifteen different models were analyzed. Using all available data, the vision transformer models provided 99.9%+ training and 97% testing accuracy across models. Vision transformer with rapid-response EEG is capable of predicting delirium. Such monitoring is feasible in critically ill older adults. Therefore, this method has strong potential for improving the accuracy of delirium detection, providing greater opportunity for individualized interventions. Such an approach may shorten hospital length of stay, increase discharge to home, decrease mortality, and reduce the financial burden associated with delirium.
Digital Object Identifier (DOI)
Publication Info
Published in Journal of the American Geriatrics Society, Volume 71, Issue 6, 2023, pages 1873-1880.
APA Citation
Mulkey, M. A., Khan, S., Perkins, A., Gao, S., Wang, S., Campbell, N., & Khan, B. (2023). Relationship between angiotensin‐converting enzyme inhibitors and angiotensin receptor blockers prescribing and delirium in the ICU‐A secondary analysis. Journal of the American Geriatrics Society, 71(6), 1873–1880. https://doi.org/10.1111/jgs.18285
Rights
© 2023 The Authors. Journal of the American Geriatrics Society published by Wiley Periodicals LLC on behalf of The American Geriatrics Society. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.