Inouye S K
Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut 06504, USA.
J Geriatr Psychiatry Neurol. 1998 Fall;11(3):118-25; discussion 157-8. doi: 10.1177/089198879801100302.
Delirium, or acute confusional state, represents a common, serious, potentially preventable and increasing problem for older hospitalized patients. This study is intended to improve overall understanding of the problem of delirium and thus to lessen its adverse impact on the older population. The specific aims of this study are (1) to examine the epidemiology of delirium in older patients; (2) to evaluate barriers to recognition; (3) to present the Confusion Assessment Method (CAM) simplified algorithm to improve recognition; (4) to elucidate predisposing and precipitating factors for delirium; and (5) to propose preventive strategies. Delirium occurs in 10-60% of the older hospitalized population and is unrecognized in 32-66% of cases. The CAM algorithm provides a sensitive (94-100%), specific (90-95%), reliable, and easy to use means for identification of delirium. Four predisposing and five precipitating factors were identified and validated to identify patients at high risk for development of delirium. Primary prevention of delirium should address important delirium risk factors and target patients at intermediate to high risk for delirium at admission.
谵妄，即急性意识模糊状态，是老年住院患者常见、严重、潜在可预防且日益增多的问题。本研究旨在提高对谵妄问题的整体认识，从而减轻其对老年人群的不利影响。本研究的具体目标包括：（1）研究老年患者谵妄的流行病学；（2）评估识别障碍；（3）介绍简化的意识模糊评估方法（CAM）算法以提高识别率；（4）阐明谵妄的易感因素和促发因素；（5）提出预防策略。10%至60%的老年住院患者会发生谵妄，其中32%至66%的病例未被识别。CAM算法为识别谵妄提供了一种敏感（94%至100%）、特异（90%至95%）、可靠且易于使用的方法。已确定并验证了四个易感因素和五个促发因素，以识别发生谵妄风险高的患者。谵妄的一级预防应针对重要的谵妄危险因素，并以入院时谵妄中度至高度风险的患者为目标。
