Mulkey Malissa A, Everhart Daniel Erik, Kim Sunghan, Olson DaiWai M, Hardin Sonya R
Malissa A. Mulkey, MSN, APRN, CCNS, CCRN, CNRN, is a neuroscience clinical nurse specialist at Duke University Hospital, Durham, North Carolina; and PhD candidate at East Carolina University, Greenville, North Carolina. Daniel Erik Everhart, PhD, ABPP, is a professor at East Carolina University, Greenville, North Carolina. Sunghan Kim, PhD, is from East Carolina University, Greenville, North Carolina. DaiWai M. Olson, PhD, RN, CCRN, FNCS, is a professor at the University of Texas Southwestern, Dallas. Sonya R. Hardin, PhD, CCRN, ACNS-BC, NP-C, is a dean and professor at University of Louisville, Kentucky.
Dimens Crit Care Nurs. 2019 Sep/Oct;38(5):241-247. doi: 10.1097/DCC.0000000000000372.
For the past 2500 years, delirium has been described based on the presence of behavioral symptoms. Each year, as many as 1 in 5 acute care and 80% of critically ill patients develop delirium. The United States spends approximately $164 million annually to combat the associated consequences of delirium. There are no laboratory tools available to assist with diagnosis and ongoing monitoring of delirium; therefore, current national guidelines for psychiatry, geriatrics, and critical care strongly recommend routine bedside screening. Despite the significance, health care teams fail to accurately identify approximately 80% of delirium episodes.The utility of conventional electroencephalogram (EEG) in the diagnosis and monitoring of delirium has been well established. Neurochemical and the associated neuroelectrical changes occur in response to overwhelming stress before behavioral symptoms; therefore, using EEG will improve early delirium identification. Adding EEG analysis to the current routine clinical assessment significantly increases the accuracy of detection. Using newer EEG technology with a limited number of leads that is capable of processing EEG may provide a viable option by reducing the cost and need for expert interpretation. Because EEG monitoring with automatic processing has become technically feasible, it could increase delirium recognition. Electroencephalogram monitoring may also provide identification before symptom onset when nursing interventions would be more effective, likely reducing the long-term ramifications. Having an objective method that nurses can easily use to detect delirium could change the standard of care and provide earlier identification.
在过去的2500年里，谵妄一直是根据行为症状来描述的。每年，多达五分之一的急性护理患者和80%的重症患者会出现谵妄。美国每年花费约1.64亿美元来应对谵妄的相关后果。目前没有实验室工具可用于协助谵妄的诊断和持续监测；因此，目前精神病学、老年医学和重症护理的国家指南强烈建议进行常规床边筛查。尽管意义重大，但医疗团队仍无法准确识别约80%的谵妄发作。传统脑电图（EEG）在谵妄诊断和监测中的作用已得到充分证实。在行为症状出现之前，神经化学和相关神经电变化会因压倒性的压力而发生；因此，使用脑电图将有助于早期识别谵妄。在当前的常规临床评估中增加脑电图分析可显著提高检测的准确性。使用导联数量有限且能够处理脑电图的更新脑电图技术，可能通过降低成本和减少专家解读的需求提供一个可行的选择。由于自动处理的脑电图监测在技术上已变得可行，它可能会提高对谵妄的识别。脑电图监测还可能在症状出现前进行识别，此时护理干预会更有效，可能会减少长期后果。拥有一种护士可以轻松用于检测谵妄的客观方法可能会改变护理标准并实现更早的识别。
