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Updated: Mar 29, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Early detection of cognitive disorders: Follow-up study
Background:
Recently, a new test (Cuetos-Vega) was developed to detect patients with early symptoms of Alzheimer's disease (AD). This test is rapid, simple and could diagnose patients at early phases of cognitive disorders. The aim of our follow-up study is to evaluate the sensitivity, specificity and predictive values of this test in primary health care (PHC) facilities.
Methods:
We obtained from our database of La Rambla (village with +/- 8,000 habitants at South Cordoba Health District) the list of asymptomatic independent patients between 66 and 75 years old. A pilot study was conducted with 20 users of La Rambla PHC Centre. This test was modified and adapted for application to our study population. All participants were selected randomly and assigned a date by telephone for the nursing outpatient clinic where the nurse proceeded to administer the screening test. All participants were invited to repeat the same test, together with Pfeiffer test (a short mental status questionnaire), 32 months after the pilot study.
Results:
The average age of participants was 71.5 ± 2.9 years and 10 (50%) were men. The sensitivity of the new test after 32 months was 50% and the specificity 90.9%. The test had a positive predictive value of 75% and a negative predictive value of 76.9%. The overall accuracy of the test was 76.4%.
Conclusion:
We present a new test designed to detect patients with mild cognitive impairment and/or early symptoms of AD. A larger cohort is needed to improve the internal validity.
