Computers in biology and medicine · 2025
Longitudinal dementia trajectories for Alzheimer's Disease characterization and prediction.
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Mori Antoine de, Tauber Clovis, Alzheimer’s Disease Neuroimaging Initiative
Аннотация
BACKGROUND: Alzheimer's Disease (AD) remains one of the most significant neurodegenerative diseases globally, affecting approximately 38.5 million people in 2023. Early identification of individuals at risk of developing AD is essential to managing disease progression and implementing timely interventions. Despite extensive research on predicting individual AD progression and diagnosis conversion, the dynamic and multimodal characterization of AD evolution remains an open challenge.
APPROACH: This study introduces a model leveraging multimodal clinical data to capture and predict both population-level and individual trajectories of dementia over extended timescales. We define a Static Dementia Score (SDS) as a metric representing the current state of dementia, with its temporal evolution modeled using a Bayesian nonlinear mixed-effects approach. This method generates a single continuous curve that delineates the average dementia progression pattern across the population. Additionally, our approach allows for patient-specific SDS progression modeling, enabling the prediction of individual dementia trajectories over a specified time horizon.
RESULTS: The model was trained on a dataset comprising 5,033 observations, including MP-RAGE MRI scans, cognitive assessments, and demographic data from 883 individuals with at least four observations and stable diagnostic trajectories. Population-level curves produced by our model align with established AD progression trends, capturing key stages of disease evolution. Individual-specific deformation parameters were effective in characterizing personalized disease progression, achieving a theoretical diagnosis prediction accuracy of 0.952±0.013 three years in advance and an AD conversion prediction accuracy of 0.916±0.022 within the same period.
CONCLUSION: Our findings underscore the potential of this approach for AD classification and forecasting disease progression. These results emphasize its utility for clinicians and researchers in detecting atypical disease trajectories early in patients with longitudinal follow-up, enhancing decision-making and therapeutic planning.
APPROACH: This study introduces a model leveraging multimodal clinical data to capture and predict both population-level and individual trajectories of dementia over extended timescales. We define a Static Dementia Score (SDS) as a metric representing the current state of dementia, with its temporal evolution modeled using a Bayesian nonlinear mixed-effects approach. This method generates a single continuous curve that delineates the average dementia progression pattern across the population. Additionally, our approach allows for patient-specific SDS progression modeling, enabling the prediction of individual dementia trajectories over a specified time horizon.
RESULTS: The model was trained on a dataset comprising 5,033 observations, including MP-RAGE MRI scans, cognitive assessments, and demographic data from 883 individuals with at least four observations and stable diagnostic trajectories. Population-level curves produced by our model align with established AD progression trends, capturing key stages of disease evolution. Individual-specific deformation parameters were effective in characterizing personalized disease progression, achieving a theoretical diagnosis prediction accuracy of 0.952±0.013 three years in advance and an AD conversion prediction accuracy of 0.916±0.022 within the same period.
CONCLUSION: Our findings underscore the potential of this approach for AD classification and forecasting disease progression. These results emphasize its utility for clinicians and researchers in detecting atypical disease trajectories early in patients with longitudinal follow-up, enhancing decision-making and therapeutic planning.