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Updated: Apr 11, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Screening in delirium: A pilot study of two screening tools, the Simple Query for Easy Evaluation of Consciousness
Hui-Shan Lin1,2, Eamonn Eeles1,2, Shaun Pandy1,2
1Department of Geriatric Medicine, The Prince Charles Hospital, Brisbane, Queensland, Australia.
Aim:
Delirium is poorly recognised and inadequately treated in medical settings. This research aimed to determine the psychometric properties of a newly developed tool, SQeeC against another emergent instrument, SQiD, in the screening of delirium.
Methods:
The SQeeC was administered to 100 patients and SQiD administered to their informants in the general medical wards of a General Hospital. Data were compared with the reference standard geriatric consultant assessment of delirium.
Results:
Compared with the reference standard, the SQeeC was found to have a sensitivity of 83% (95% CI 52-98%) and a specificity of 81% (95% CI 72-89%) with a negative predictive value of 97% (95% CI 90-100%) while the SQiD was found to have a sensitivity of 77% (95% CI 56-91%), a specificity of 51% (95% CI 37-64%) and a negative predictive value of 83% (95% CI 66-93%).
Conclusion:
The SQeeC and SQiD are simple and time efficient screening tools with encouraging psychometric properties.
