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I-Corps: Delirium Prediction and Screening by Non-Invasive Point-of-Care

I-Corps: Delirium Prediction and Screening by Non-Invasive Point-of-Care
I-Corps:通过非侵入性护理点进行谵妄预测和筛查
批准号:
1664364
负责人:
Gen Shinozaki
金额:
$5.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-11-01 至 2018-04-30

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英文摘要
The broader impacts and commercial potential of this I-Corps project is to reduce negative outcomes associated with delirium. Delirium is a dangerous state of confusion, with one year mortality as high as 40%, is very common, 20~50% among general medicine unit, and 70-80% among intensive care unit (ICU), affecting 2-3 million hospitalized elderly patients annually. Delirium can result increased hospital stay and increased healthcare costs. To improve the outcomes associated with delirium, it is critical to detect delirium early on and initiate appropriate treatment for delirium and the underlining conditions in a timely manner. Although, various screening instruments and rating scales for delirium have been implemented in the hospitals, it is still extremely difficult to identify delirium, because such instruments requires enough training for hospital staffs, and also there is a challenge of subjectivity. Thus, such instruments have been shown to have low sensitivity, especially in busy hospital settings such as ICU. As we live in an aging society, we need better methods for early detection in more objective manner.This I-Corps project is to detect delirium early on using a newly created spectral density algorithm to analyze patient's brainwave signals recorded by a simplified bispectral electroencephalography (EEG) device. Although it has been shown that generalized slowing brainwave is characteristic to delirium, due to its complexity of application and necessity of interpretation by neurology specialist, traditional EEG is not suitable for mass screening of large volume of high risk elderly patients. However, power spectrum analysis can reliably calculate different frequency bands of the EEG signals after filtering artifact effects and has been reported to detect characteristic EEGs in delirious patients. A preliminary data from a pilot study has shown very promising data differentiating delirium and normal condition. With continuing efforts to validate the algorithm and to examine the efficacy of the simplified bispectral EEG in mass screening for delirium, it is the goal of the project to implement the approach using simplified EEG device to assist healthcare providers in clinical practice of delirium assessment and management for better outcomes with less mortality, shorter lengths of hospital stay, and lower healthcare cost.
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