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Predicting postoperative delirium after cardiovascular surgeries from preoperative portable electroencephalography oscillations
INTRODUCTION: Postoperative delirium (POD) is common and life-threatening, however, with intensive interventions, a potentially preventable clinical syndrome. Although electroencephalography (EEG) is a promising biomarker of delirium, standard 20-leads EEG holds difficulties for screening usage in c...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10682064/ https://www.ncbi.nlm.nih.gov/pubmed/38034919 http://dx.doi.org/10.3389/fpsyt.2023.1287607 |
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author | Hata, Masahiro Miyazaki, Yuki Nagata, Chie Masuda, Hirotada Wada, Tamiki Takahashi, Shun Ishii, Ryouhei Miyagawa, Shigeru Ikeda, Manabu Ueno, Takayoshi |
author_facet | Hata, Masahiro Miyazaki, Yuki Nagata, Chie Masuda, Hirotada Wada, Tamiki Takahashi, Shun Ishii, Ryouhei Miyagawa, Shigeru Ikeda, Manabu Ueno, Takayoshi |
author_sort | Hata, Masahiro |
collection | PubMed |
description | INTRODUCTION: Postoperative delirium (POD) is common and life-threatening, however, with intensive interventions, a potentially preventable clinical syndrome. Although electroencephalography (EEG) is a promising biomarker of delirium, standard 20-leads EEG holds difficulties for screening usage in clinical practice. OBJECTIVE: We aimed to develop an accurate algorithm to predict POD using EEG data obtained from portable device. METHODS: We recruited 128 patients who underwent scheduled cardiovascular surgery. Cognitive function assessments were conducted, and portable EEG recordings were obtained prior to surgery. RESULTS: Among the patients, 47 (36.7%) patients with POD were identified and they did not significantly differ from patients without POD in sex ratio, age, cognitive function, or treatment duration of intensive care unit. However, significant differences were observed in the preoperative EEG power spectrum densities at various frequencies, especially gamma activity, between patients with and without POD. POD was successfully predicted using preoperative EEG data with a machine learning algorithm, yielding accuracy of 86% and area under the receiver operating characteristic curve of 0.93. DISCUSSION: This study provides new insights into the objective and biological vulnerability to delirium. The developed algorithm can be applied in general hospitals without advanced equipment and expertise, thereby enabling the reduction of POD occurrences with intensive interventions for high-risk patients. |
format | Online Article Text |
id | pubmed-10682064 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-106820642023-11-30 Predicting postoperative delirium after cardiovascular surgeries from preoperative portable electroencephalography oscillations Hata, Masahiro Miyazaki, Yuki Nagata, Chie Masuda, Hirotada Wada, Tamiki Takahashi, Shun Ishii, Ryouhei Miyagawa, Shigeru Ikeda, Manabu Ueno, Takayoshi Front Psychiatry Psychiatry INTRODUCTION: Postoperative delirium (POD) is common and life-threatening, however, with intensive interventions, a potentially preventable clinical syndrome. Although electroencephalography (EEG) is a promising biomarker of delirium, standard 20-leads EEG holds difficulties for screening usage in clinical practice. OBJECTIVE: We aimed to develop an accurate algorithm to predict POD using EEG data obtained from portable device. METHODS: We recruited 128 patients who underwent scheduled cardiovascular surgery. Cognitive function assessments were conducted, and portable EEG recordings were obtained prior to surgery. RESULTS: Among the patients, 47 (36.7%) patients with POD were identified and they did not significantly differ from patients without POD in sex ratio, age, cognitive function, or treatment duration of intensive care unit. However, significant differences were observed in the preoperative EEG power spectrum densities at various frequencies, especially gamma activity, between patients with and without POD. POD was successfully predicted using preoperative EEG data with a machine learning algorithm, yielding accuracy of 86% and area under the receiver operating characteristic curve of 0.93. DISCUSSION: This study provides new insights into the objective and biological vulnerability to delirium. The developed algorithm can be applied in general hospitals without advanced equipment and expertise, thereby enabling the reduction of POD occurrences with intensive interventions for high-risk patients. Frontiers Media S.A. 2023-11-14 /pmc/articles/PMC10682064/ /pubmed/38034919 http://dx.doi.org/10.3389/fpsyt.2023.1287607 Text en Copyright © 2023 Hata, Miyazaki, Nagata, Masuda, Wada, Takahashi, Ishii, Miyagawa, Ikeda and Ueno. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychiatry Hata, Masahiro Miyazaki, Yuki Nagata, Chie Masuda, Hirotada Wada, Tamiki Takahashi, Shun Ishii, Ryouhei Miyagawa, Shigeru Ikeda, Manabu Ueno, Takayoshi Predicting postoperative delirium after cardiovascular surgeries from preoperative portable electroencephalography oscillations |
title | Predicting postoperative delirium after cardiovascular surgeries from preoperative portable electroencephalography oscillations |
title_full | Predicting postoperative delirium after cardiovascular surgeries from preoperative portable electroencephalography oscillations |
title_fullStr | Predicting postoperative delirium after cardiovascular surgeries from preoperative portable electroencephalography oscillations |
title_full_unstemmed | Predicting postoperative delirium after cardiovascular surgeries from preoperative portable electroencephalography oscillations |
title_short | Predicting postoperative delirium after cardiovascular surgeries from preoperative portable electroencephalography oscillations |
title_sort | predicting postoperative delirium after cardiovascular surgeries from preoperative portable electroencephalography oscillations |
topic | Psychiatry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10682064/ https://www.ncbi.nlm.nih.gov/pubmed/38034919 http://dx.doi.org/10.3389/fpsyt.2023.1287607 |
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