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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
The RAPid COmmunity COGnitive screening Programme (RAPCOG): Developing the Portuguese version of the Quick Mild
P Machado Dos Santos1, R O'Caoimh2,3, A Svendrovski4
1Center for Health Technology and Services Research/ICBAS, Institute of Biomedical Sciences Abel Salazar - University of Porto, Porto, Portugal.
Abstract:
As populations age and the prevalence of cognitive impairment increases, healthcare professionals and researchers require short, validated cognitive screening instruments (CSIs). As part the EIP-on-AHA Twinning Support Scheme (2016), four reference sites developed the RAPid COmmunity COGnitive screening Programme (RAPCOG) twinning project to validate translated versions of the Quick Mild Cognitive Impairment (Qmci) screen that could be adapted quickly for use with future eHealth screening and assessment programmes. Here we present the cultural adaption and translation of the Qmci-Portuguese (Qmci-P) screen as part of RAPCOG and explore its subsequent validation against two commonly-used CSIs (MMSE-P and MoCA-P) with 93 participants aged ≥65, attending ten day care centres or resident in two long-term care institutions; median age 74 (+/-15), 66% female. The Qmci-P's internal consistency was high (Cronbach's Alpha 0.82), compared with the MoCA (0.79) and SMMSE (0.54). Qmci-P screen scores moderately correlated with the SMMSE (r=0.61, 95% CI:0.45-0.72, p<0.001) and MoCA (r=0.63, 95% CI:0.36-0.80, p<0.001). The Qmci-P screen demonstrates high internal consistency and concurrent validity against more established CSIs and given its brevity (3-5mins), may be preferable for use in community settings. This project shows the potential of the EIP-on-AHA Twinning initiative to promote the scaling-up of innovative good practices.
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