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Updated: May 3, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Development and validation of a brief dementia screening indicator for primary care
Deborah E Barnes1, Alexa S Beiser2, Anne Lee3
1Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Epidemiology & Biostatistics, University of California, San Francisco, CA, USA; Veterans Affairs Medical Center, San Francisco, CA, USA.
Background:
Detection of "any cognitive impairment" is mandated as part of the Medicare annual wellness visit, but screening all patients may result in excessive false positives.
Methods:
We developed and validated a brief Dementia Screening Indicator using data from four large, ongoing cohort studies (the Cardiovascular Health Study [CHS]; the Framingham Heart Study [FHS]; the Health and Retirement Study [HRS]; the Sacramento Area Latino Study on Aging [SALSA]) to help clinicians identify a subgroup of high-risk patients to target for cognitive screening.
Results:
The final Dementia Screening Indicator included age (1 point/year; ages, 65-79 years), less than 12 years of education (9 points), stroke (6 points), diabetes mellitus (3 points), body mass index less than 18.5 kg/m(2) (8 points), requiring assistance with money or medications (10 points), and depressive symptoms (6 points). Accuracy was good across the cohorts (Harrell's C statistic: CHS, 0.68; FHS, 0.77; HRS, 0.76; SALSA, 0.78).
Conclusions:
The Dementia Screening Indicator is a simple tool that may be useful in primary care settings to identify high-risk patients to target for cognitive screening.
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