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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Brief Cognitive Tests Used in Primary Care Cannot Accurately Differentiate Mild Cognitive Impairment from Subjective
Ferdinando Petrazzuoli1, Susanna Vestberg2, Patrik Midlöv1
1Center for Primary Health Care Research, Clinical Research Center, Lund University, Malmö, Sweden.
Background:
Differentiating mild cognitive impairment (MCI) from subjective cognitive decline (SCD) is important because of the higher progression rate to dementia for MCI and when considering future disease-modifying drugs that will have treatment indications at the MCI stage.
Objective:
We examined if the two most widely-used cognitive tests, the Mini-Mental State Examination (MMSE) and clock-drawing test (CDT), and a test of attention/executive function (AQT) accurately can differentiate MCI from SCD.
Methods:
We included 466 consecutively recruited non-demented patients with cognitive complaints from the BioFINDER study who had been referred to memory clinics, predominantly from primary care. They were classified as MCI (n = 258) or SCD (n = 208) after thorough neuropsychological assessments. The accuracy of MMSE, CDT, and AQT for identifying MCI was examined both in training and validation samples and in the whole population.
Results:
As a single test, MMSE had the highest accuracy (sensitivity 73%, specificity 60%). The best combination of two tests was MMSE < 27 points or AQT > 91 seconds (sensitivity 56%, specificity 78%), but in logistic regression models, their AUC (0.76) was not significantly better than MMSE alone (AUC 0.75). CDT and AQT performed significantly worse (AUC 0.71; p < 0.001-0.05); otherwise no differences were seen between any combination of two or three tests.
Conclusion:
Neither single nor combinations of tests could differentiate MCI from SCD with adequately high accuracy. There is a great need to further develop, validate, and implement accurate screening-tests for primary care to improve accurate identification of MCI among individuals that seek medical care due to cognitive symptoms.
Insights
Differentiating mild cognitive impairment (MCI) from subjective cognitive decline (SCD) is crucial. Standard cognitive tests like MMSE and AQT showed limited accuracy in distinguishing between MCI and SCD in primary care settings.
Area of Science:
- Neurology
- Cognitive Science
- Geriatrics
Background:
- Distinguishing mild cognitive impairment (MCI) from subjective cognitive decline (SCD) is critical due to MCI's higher progression rate to dementia.
- Accurate differentiation is essential for timely intervention and eligibility for future disease-modifying therapies indicated at the MCI stage.
Purpose of the Study:
- To evaluate the accuracy of the Mini-Mental State Examination (MMSE), clock-drawing test (CDT), and Attention/Executive Functioning Questionnaire (AQT) in differentiating MCI from SCD.
- To assess the efficacy of single tests and combinations of these cognitive assessments in a primary care setting.
Main Methods:
- A cohort of 466 non-demented patients with cognitive complaints, referred to memory clinics, were classified as MCI (n=258) or SCD (n=208).
- The diagnostic accuracy of MMSE, CDT, and AQT was assessed individually and in combination using training and validation samples.
Main Results:
- The MMSE demonstrated the highest accuracy as a single test (sensitivity 73%, specificity 60%).
- The combination of MMSE < 27 or AQT > 91 seconds showed improved specificity (78%) but not significantly better overall accuracy (AUC 0.76) compared to MMSE alone (AUC 0.75).
- CDT and AQT performed significantly worse than MMSE, with no significant improvements noted for any combination of two or three tests.
Conclusions:
- Current cognitive screening tests, including MMSE, CDT, and AQT, lack sufficient accuracy to reliably differentiate MCI from SCD in individuals seeking medical attention for cognitive symptoms.
- There is a significant need for the development and validation of more accurate screening tools for primary care to enhance the early identification of MCI.
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