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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Predicting conversion of patients with Mild Cognitive Impairment to Alzheimer's disease using bedside cognitive
Abby Clarke1, Calvin Ashe1, Jill Jenkinson1
1Department of Psychology, Maynooth University, Maynooth, Ireland.
Introduction:
Patients diagnosed with Mild Cognitive Impairment (MCI) often go on to develop dementia, however many do not. Although cognitive tests are widely used in the clinic, there is limited research on their potential to help predict which patients may progress to Alzheimer's disease (AD) from those that do not.
Methods:
MCI patients (n = 325) from the longitudinal Alzheimer's Disease Neuroimaging Initiative (ADNI-2) dataset were tracked across a 5 year period. Upon initial diagnosis, all patients underwent a series of cognitive tests including the Mini Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) and Alzheimer's Disease Assessment Scale-Cognitive (ADAS-Cog 13). Twenty-five percent (n = 83) of those initially diagnosed with MCI subsequently developed AD within 5 years.
Results:
We showed that those individuals that progressed to AD had significantly lower scores upon baseline testing on the MMSE and MoCA, and higher scores on the ADAS-13, compared to those that did not convert. However, not all tests were equivalent. We showed that the ADAS-13 offers the best predictability of conversion (Adjusted Odds ratio (AOR) = 3.91). This predictability was higher than that offered by the two primary biomarker Amyloid-beta (Aβ, AOR = 1.99) and phospho-tau (Ptau, AOR = 1.72). Further analysis on the ADAS-13 showed that MCI patients that subsequently converted to AD performed particularly poorly on delayed-recall (AOR = 1.93), word recognition (AOR = 1.66), word finding difficulty (AOR = 1.55) and orientation (1.38) test items.
Conclusions:
Cognitive testing using the ADAS-13 may offer a simpler, less invasive, more clinically relevant and a more effective method of determining those that are in danger of converting from MCI to AD.
Insights
The Alzheimer's Disease Assessment Scale-Cognitive (ADAS-13) best predicts progression from Mild Cognitive Impairment (MCI) to Alzheimer's disease (AD) compared to other cognitive tests and biomarkers. This cognitive test offers a simpler, more effective method for identifying at-risk patients.
Area of Science:
- Neurology
- Cognitive Science
- Biomarker Research
Background:
- Mild Cognitive Impairment (MCI) is a precursor to dementia, but not all individuals with MCI progress to Alzheimer's disease (AD).
- Predicting conversion from MCI to AD is crucial for timely intervention and clinical trial enrollment.
- Current cognitive tests have limitations in accurately predicting AD progression.
Purpose of the Study:
- To evaluate the predictive accuracy of different cognitive tests for conversion from MCI to AD.
- To compare the predictive power of cognitive tests against established AD biomarkers (Amyloid-beta and phospho-tau).
Main Methods:
- Utilized data from 325 MCI patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI-2) dataset, followed for 5 years.
- Administered baseline cognitive tests: Mini Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Alzheimer's Disease Assessment Scale-Cognitive (ADAS-Cog 13).
- Assessed conversion to AD, with 25% (n=83) progressing within 5 years.
Main Results:
- Individuals progressing to AD showed significantly lower MMSE and MoCA scores and higher ADAS-13 scores at baseline.
- The ADAS-13 demonstrated the highest predictability for MCI to AD conversion (Adjusted Odds Ratio [AOR] = 3.91), outperforming Amyloid-beta (AOR = 1.99) and phospho-tau (AOR = 1.72).
- Specific ADAS-13 sub-items, including delayed recall and word recognition, were particularly indicative of conversion.
Conclusions:
- The ADAS-13 is a highly effective cognitive test for predicting conversion from MCI to AD.
- ADAS-13 offers a more accessible and clinically relevant predictive tool than current biomarkers.
- Routine use of ADAS-13 can aid clinicians in identifying MCI patients at higher risk of progressing to Alzheimer's disease.
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