Department of Geriatrics, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Department of Nephrology, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Age Ageing. 2023 May 1;52(5). doi: 10.1093/ageing/afad074.
Delirium is a common complication clinically and is associated with the poor outcomes, yet it is frequently unrecognised and readily disregarded. Although the 3-minute diagnostic interview for confusion assessment method-defined delirium (3D-CAM) has been used in a variety of care settings, a comprehensive evaluation of its accuracy in all available care settings has not been performed.
This study aimed to evaluate the diagnostic test accuracy of the 3D-CAM in delirium detection through a systematic review and meta-analysis.
We systematically searched PubMed, EMBASE, the Cochrane Library, Web of Science, CINAHL (EBSCO) and ClinicalTrials.gov published from inception to 10 July 2022. The quality assessment of the diagnostic accuracy studies-2 tool was applied to evaluate methodological quality. A bivariate random effects model was used to pool sensitivity and specificity.
Seven studies with 1,350 participants and 2,499 assessments were included, which were carried out in general medical wards, intensive care units, internal medical wards, surgical wards, recovery rooms and post-anaesthesia care units. The prevalence of delirium ranged from 9.1% to 25%. The pooled sensitivity and specificity were 0.92 (95% confidence interval [CI] 0.87-0.95) and 0.95 (95% CI 0.92-0.97), respectively. The pooled positive likelihood ratio was 18.6 (95% CI 12.2-28.2), the negative likelihood ratio was 0.09 (95% CI 0.06-0.14) and the diagnostic odds ratio was 211 (95% CI 128-349). Moreover, the area under the curve was 0.97 (95% CI 0.95-0.98).
The 3D-CAM has good diagnostic accuracy for delirium detection in different care settings. Further analyses illustrated that it had comparable diagnostic accuracy in older adults and patients with dementia or known baseline cognitive impairment. In conclusion, the 3D-CAM is recommended for clinical delirium detection.
谵妄是一种常见的临床并发症，与不良预后相关，但常被忽视。虽然 3 分钟诊断性访谈用于混淆评估方法定义的谵妄（3D-CAM）已在各种护理环境中使用，但尚未对其在所有可用护理环境中的准确性进行全面评估。
本研究旨在通过系统评价和荟萃分析评估 3D-CAM 对谵妄检测的诊断测试准确性。
我们系统地检索了 PubMed、EMBASE、Cochrane 图书馆、Web of Science、CINAHL（EBSCO）和 ClinicalTrials.gov，检索时间从建库至 2022 年 7 月 10 日。采用诊断准确性研究-2 工具评估方法学质量。采用双变量随机效应模型计算敏感性和特异性。
纳入了 7 项研究，共 1350 名参与者和 2499 次评估，这些研究在普通内科病房、重症监护病房、内科病房、外科病房、恢复室和术后麻醉护理单元进行。谵妄的患病率范围为 9.1%至 25%。汇总的敏感性和特异性分别为 0.92（95%置信区间[CI]0.87-0.95）和 0.95（95%CI0.92-0.97）。汇总的阳性似然比为 18.6（95%CI12.2-28.2），阴性似然比为 0.09（95%CI0.06-0.14），诊断比值比为 211（95%CI128-349）。此外，曲线下面积为 0.97（95%CI0.95-0.98）。
3D-CAM 对不同护理环境中的谵妄检测具有良好的诊断准确性。进一步分析表明，它在老年患者和痴呆或已知基线认知障碍患者中的诊断准确性相当。总之，3D-CAM 推荐用于临床谵妄检测。
