Diagnostic accuracy of the 3-minute diagnostic interview for confusion assessment method-defined delirium in delirium detection: a systematic review and meta-analysis

Abstract

Abstract Background 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. Objective This study aimed to evaluate the diagnostic test accuracy of the 3D-CAM in delirium detection through a systematic review and meta-analysis. Methods 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. Results 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). Conclusions 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.