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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Real-word application of the AT(N) classification and disease-modifying treatment eligibility in a hospital-based
Elisa Canu1, Giulia Rugarli1,2,3, Federico Coraglia1,3
1Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.
Background And Objectives:
The AT(N) classification system stratifies patients based on biomarker profiles, including amyloid-beta deposition (A), tau pathology (T), and neurodegeneration (N). This study aims to apply the AT(N) classification to a hospital-based cohort of patients with cognitive decline and/or dementia, within and outside the Alzheimer's disease (AD) continuum, to enhance our understanding of the multidimensional aspects of AD and related disorders. Furthermore, we wish to investigate how many cases from our cohort would be eligible for the available disease modifying treatments, such as aducanemab and lecanemab.
Methods:
We conducted a retrospective evaluation of 429 patients referred to the Memory Center of IRCCS San Raffaele Hospital in Milan. Patients underwent clinical/neuropsychological assessments, lumbar puncture, structural brain imaging, and positron emission tomography (FDG-PET). Patients were stratified according to AT(N) classification, group comparisons were performed and the number of eligible cases for anti-β amyloid monoclonal antibodies was calculated.
Results:
Sociodemographic and clinical features were similar across groups. The most represented group was A + T + N + accounting for 38% of cases, followed by A + T - N + (21%) and A - T - N + (20%). Although the clinical presentation was similar, the A + T + N + group showed more severe cognitive impairment in memory, language, attention, executive, and visuospatial functions compared to other AT(N) groups. Notably, T + patients demonstrated greater memory complaints compared to T - cases. FDG-PET outperformed MRI and CT in distinguishing A + from A - patients. Although 61% of the observed cases were A + , only 17% of them were eligible for amyloid-targeting treatments.
Discussion:
The AT(N) classification is applicable in a real-world clinical setting. The classification system provided insights into clinical management and treatment strategies. Low cognitive performance and specific regional FDG-PET hypometabolism at diagnosis are highly suggestive for A + T + or A - T + profiles. This work provides also a realistic picture of the proportion of AD patients eligible for disease modifying treatments emphasizing the need for early detection.
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