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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Non-literacy biased, culturally fair cognitive detection tool in primary care patients with cognitive concerns: a
Joe Verghese1,2, Rachel Chalmer3, Marnina Stimmel4
1Departments of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA. joe.verghese@einsteinmed.edu.
Abstract:
Dementia is often undiagnosed in primary care, and even when diagnosed, untreated. The 5-Cog paradigm, a brief, culturally adept, cognitive detection tool paired with a clinical decision support may reduce barriers to improving dementia diagnosis and care. We performed a randomized controlled trial in primary care patients experiencing health disparities (racial/ethnic minorities and socioeconomically disadvantaged). Older adults with cognitive concerns were assigned in a 1:1 ratio to the 5-Cog paradigm or control. Primary outcome was improved dementia care actions defined as any of the following endpoints within 90 days: new mild cognitive impairment syndrome or dementia diagnoses as well as investigations, medications or specialist referrals ordered for cognitive indications. Groups were compared using intention-to-treat principles with multivariable logistic regression. Overall, 1,201 patients (mean age 72.8 years, 72% women and 94% Black, Hispanic or Latino) were enrolled and 599 were assigned to 5-Cog and 602 to the control. The 5-Cog paradigm demonstrated threefold odds of improvement in dementia care actions over control (odds ratio 3.43, 95% confidence interval 2.32-5.07). No serious intervention-related adverse events were reported. The 5-Cog paradigm improved diagnosis and management in patients with cognitive concerns and provides evidence to promote practice change to improve dementia care actions in primary care.ClinicalTrials.gov: NCT03816644 .
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