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Validation and demonstration of a new comprehensive model of Alzheimer's disease progression
Author(s) -
Stern Yaakov,
Stallard Eric,
Kinosian Bruce,
Zhu Carolyn,
Cosentino Stephanie,
Jin Zhezhen,
Gu Yian
Publication year - 2021
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.12336
Subject(s) - disease , cohort , dependency (uml) , computer science , medicine , artificial intelligence
Abstract Introduction Identifying the course of Alzheimer's disease (AD) for individual patients is important for numerous clinical applications. Ideally, prognostic models should provide information about a range of clinical features across the entire disease process. Previously, we published a new comprehensive longitudinal model of AD progression with inputs/outputs covering 11 interconnected clinical measurement domains. Methods Here, we (1) validate the model on an independent cohort; and (2) demonstrate the model's utility in clinical applications by projecting changes in 6 of the 11 domains. Results Survival and prevalence curves for two representative outcomes—mortality and dependency—generated by the model accurately reproduced the observed curves both overall and for patients subdivided according to risk levels using an independent Cox model. Discussion The new model, validated here, effectively reproduces the observed course of AD from an initial visit assessment, allowing users to project coordinated developments for individual patients of multiple disease features.