Abstract
Alzheimer’s disease is the most common neurodegenerative disease and is characterized by the accumulation of amyloid-beta peptides leading to the formation of plaques and tau protein tangles in brain. These neuropathological features precede cognitive impairment and Alzheimer’s dementia by many years. To better understand and predict the course of disease from early-stage asymptomatic to late-stage dementia, it is critical to study the patterns of progression of multiple markers. In particular, we aim to predict the likely future course of progression for individuals given only a single observation of their markers. Improved individual-level prediction may lead to improved clinical care and clinical trials. We propose a two-stage approach to modeling and predicting measures of cognition, function, brain imaging, fluid biomarkers, and diagnosis of individuals using multiple domains simultaneously. In the first stage, joint (or multivariate) mixed-effects models are used to simultaneously model multiple markers over time. In the second stage, random forests are used to predict categorical diagnoses (cognitively normal, mild cognitive impairment, or dementia) from predictions of continuous markers based on the first-stage model. The combination of the two models allows one to leverage their key strengths in order to obtain improved accuracy. We characterize the predictive accuracy of this two-stage approach using data from the Alzheimer’s Disease Neuroimaging Initiative. The two-stage approach using a single joint mixed-effects model for all continuous outcomes yields better diagnostic classification accuracy compared to using separate univariate mixed-effects models for each of the continuous outcomes. Overall prediction accuracy above 80% was achieved over a period of 2.5 years. The results further indicate that overall accuracy is improved when markers from multiple assessment domains, such as cognition, function, and brain imaging, are used in the prediction algorithm as compared to the use of markers from a single domain only.
Highlights
Prediction of future Alzheimer’s disease (AD)-related progression is extremely valuable in clinical practice and in medical research
We focus on the following assessments: Alzheimer’s Disease Assessment— Cognitive 13-item scale (ADAS13), Clinical Dementia Rating—Sum of Boxes (CDRSB), Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MOCA), Rey Auditory Verbal Learning Test Immediate (RAVLT Immediate), Everyday Cognition (ECog)—total by participant (ECogPtTotal) and study partner (ECogSPTotal) and Functional Assessment Questionnaire (FAQ)
3 Methodology We propose a two-stage approach for prediction of continuous disease markers and categorical diagnosis
Summary
Prediction of future Alzheimer’s disease (AD)-related progression is extremely valuable in clinical practice and in medical research. The ability to accurately predict the diagnosis of a patient can help physicians make more informed clinical decisions on treatment strategies [1]. The pathology of AD is characterized by the accumulation of amyloid plaques and neurofibrillary tangles in the brain beginning as early as middle age. Recent studies have shown that the pathology of the disease occurs several years before the onset of clinical symptoms, making the disease difficult to detect at an early stage [5, 6]. Prediction of the future diagnosis of an individual (CN, MCI, or dementia) is very challenging due to high subjectivity and individual-level variability in cognitive assessments and levels of biomarkers, which have typically been used for staging of AD. The assessment of an individual’s current diagnosis can vary from one clinician to the or from one day to the
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