Related Experiment Video
Updated: Jul 18, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Accuracy of a clinical decision support system based on the 3-minute diagnostic interview for CAM-defined delirium: A
Jiamin Wang1, Meihua Ji2, Yongjun Huang3
1School of Nursing, Beijing University of Chinese Medicine, 100105, Beijing, China; School of Nursing, Capital Medical University, 100069, Beijing, China.
Objective:
To evaluate the accuracy of the 3D-DST for delirium assessment in older adults by the nurse researcher.
Methods:
The 3D-DST was administered by a trained nurse researcher to assess delirium among eligible older adults (aged ≥70 years). The criteria for identifying delirium was based on the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-V).
Results:
A total of 95 older adults were enrolled in the current study, and 23 patients were identified as positive for delirium by the psychiatrist. The sensitivity and specificity of the 3D-DST were 96% and 94%, respectively. High sensitivities of the 3D-DST were also observed among patients with hypoactive delirium (95%) and those with cognitive impairment (93%).
Conclusion:
The 3D-DST was demonstrated as an appropriate instrument with highly acceptable sensitivities and specificities for delirium detection in hospitalized older patients.
