Longitudinal Sensitivity of Alzheimer’s Disease Severity Staging

Abstract

Understanding Alzheimer’s disease (AD) dynamics is essential in diagnosis and measuring progression for clinical decision-making; however, clinical instruments are imperfect at classifying true disease stages. This research evaluates sensitivity and determinants of AD stage changes longitudinally using current classifications of “mild,” “moderate,” and “severe” AD, using Mini-Mental State Examination (MMSE), Alzheimer’s Disease Assessment Scale–Cognitive subscale (ADAS-Cog), and the Clinical Dementia Rating–Sum of Boxes (CDR-SB) thresholds. Age and pre-progression rate were significant determinants of AD progression using MMSE alone to stage AD, and pre-progression was found to impact disease progression with CDR-SB. Sensitivity of these instruments for identifying clinical stages of AD to correctly staging a “moderate” level of disease severity for outcomes MMSE, CDR-SB, and ADAS-Cog was 92%, 78%, and 92%, respectively. This research derives longitudinal sensitivity of clinical instruments used to stage AD useful for clinical decision-making. The MMSE and ADAS-Cog provided adequate sensitivity to classify AD stages.

Association of MRI-measured cerebral ventricular volume with APOE ε4 genotype, cerebrospinal fluid biomarkers (Aβ42 and Tau) and neuropsychological measures in Alzheimer’s disease: A Systematic Review

“…These combinations can therefore be potentially useful biomarkers for tracking the progression of AD, however there is a stronger support for using the relationship between MMSE and ventricular volume. This is also supported in a recent study which found that the sensitivity of MMSE (∼92%) (but similar to that of ADAS-cog) supersede that of CDR-sum (∼78%) for the identification of changes associated with the clinical stages of AD longitudinally (91).…”

Combining Multimodal Behavioral Data of Gait, Speech, and Drawing for Classification of Alzheimer’s Disease and Mild Cognitive Impairment

“…AD patients fulfilled the National Institute on Aging and Alzheimer's Association (NIA-AA) criteria for probable AD dementia [37]. They also fulfilled the AD Neuroimaging Initiative (ADNI) criteria for AD in terms of MMSE, CDR, and LM-IIA scores [38], except that we included patients with moderate AD based on Benoit et al's criteria [39]. Specifically, AD patients fulfilled the following criteria: MMSE score 10-26; the CDR global score 0.5 or 1; and LM-IIA score ≤8 for 16 years of education (YoE), ≤4 for 8-15 YoE, and ≤2 for 0-7 YoE.…”

Diagnosis of Alzheimer’s Disease Severity with fMRI Images Using Robust Multitask Feature Extraction Method and Convolutional Neural Network (CNN)

“…Due to science, computer-aided diagnosis systems (CADs) were developed to play an important role in enhancing the understanding of medical imagery among researchers and physicians. The application of the machine learning technique, in particular DL strategies in CAD models to diagnose and classify stable control patients with average (CN), AD, and mild cognitive impairment (MCI), has exponentially grown [ 21 , 22 ]. The automatic diagnosis of AD performs an essential role in human health, especially in the early stages.…”

“…Currently, several scholars have discussed using image classification to carry out AD diagnosis. Several DL approaches have been suggested to use MRI images to introduce multiple AD patients' severity [ 22 , 23 ]. The higher the image quality, the better the outcomes achieved, known in image analysis.…”