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: $ 5万
• 依托单位: University of Iowa
• 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
• 起止时间: 2016-11-01 至 2018-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1664364&HistoricalAwards=false
• 关键词: Corps; Delirium; Prediction; Screening; Non

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.

这个I-Corps项目的更广泛的影响和商业潜力是减少与谵妄相关的负面结果。谵妄是一种危险的意识混乱状态，一年死亡率高达40%，非常常见，在普通内科病房为20~50%，在重症监护病房（ICU）为70-80%，每年影响200 ~ 300万住院老年患者。 谵妄可导致住院时间延长和医疗费用增加。为了改善与谵妄相关的结局，早期发现谵妄并及时对谵妄和基础疾病进行适当治疗至关重要。尽管医院已经实施了各种谵妄筛查仪器和评定量表，但识别谵妄仍然极其困难，因为这些仪器需要对医院工作人员进行足够的培训，而且存在主观性的挑战。因此，这种仪器已经显示出具有低灵敏度，特别是在诸如ICU的忙碌医院环境中。随着我们生活在老龄化社会，我们需要更好的方法以更客观的方式进行早期检测。I-Corps的这个项目是使用新创建的谱密度算法来分析由简化的双谱脑电描记术（EEG）记录的患者脑波信号，以早期检测谵妄。设备。虽然广泛性慢化脑电波是谵妄的特征性表现，但由于其应用的复杂性和需要神经科专家的解释，传统的脑电图不适合于大批量高危老年患者的筛查。然而，功率谱分析可以可靠地计算滤除伪影后的EEG信号的不同频带，并已被报道用于检测谵妄患者的特征EEG。初步研究的初步数据显示，区分谵妄和正常状态的数据非常有希望。随着验证算法和检查简化的双频谱EEG在大规模筛查谵妄中的有效性的持续努力，该项目的目标是使用简化的EEG设备来实施该方法，以帮助医疗保健提供者在临床实践中进行谵妄评估和管理，以获得更好的结局，降低死亡率，缩短住院时间，降低医疗保健成本。