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Updated: Jul 23, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Development and pilot testing of the revised Patients' Attitudes Towards Deprescribing questionnaire for people with
Emily Reeve1,2,3, Allicia C Anthony4,5, Lisa Kouladjian O'Donnell1,4
1NHMRC Cognitive Decline Partnership Centre, Faculty of Medicine and Health, Kolling Institute of Medical Research, Northern Clinical School, University of Sydney, Sydney, New South Wales, Australia.
Objectives:
(i) To develop a version of the revised Patients' Attitude Towards Deprescribing (rPATD) questionnaire for people with mild cognitive impairment (MCI) and mild-to-moderate dementia (rPATDcog); and (ii) to capture the beliefs and attitudes of this population and their carers about deprescribing through a pilot study.
Methods:
First, the rPATDcog was modified from the rPATD and tested in a small group of participants with MCI or mild dementia, and then, we conducted a pilot study of the rPATDcog and the carers' rPATD.
Results:
Twenty-one participants with cognitive impairment and 11 carer participants (forming dyads) participated in this study. Eighty-one per cent (17/21) of participants said that they would stop one or more of their medications if their doctor said it was possible. There was an excellent agreement of corresponding questions between dyads.
Conclusion:
The rPATDcog was feasible to administer in this study. Further work is required to provide generalisable results and determine utility in practice.
